---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20986'
abstract:
- lang: eng
text: During complex vocal interactions, different features of acoustic stimuli
are integrated to produce appropriate vocal responses,1 such as copying sounds
during vocal matching behavior in some animals.2,3,4,5,6,7,8,9,10,11,12 However,
little is known about the interplay and possible trade-offs between the different
temporal and spectral acoustic features during these vocal exchanges.2,13,14 Nightingales
can flexibly match the pitch of their tonal “whistle songs” in real time during
counter-singing duels.15,16 Here, we show that the syllable duration of whistle
playbacks could alter the song responses of wild nightingales, causing their whistle
duration distribution to shift toward the presented stimulus duration. When exposed
to whistle playbacks featuring unnatural combinations of pitch and duration, nightingales
demonstrate a flexible trade-off between pitch matching and temporal imitation,
yet they are constrained by their vocal repertoire. They selectively adapted their
vocal responses to approximate these novel stimuli, aligning them with their natural
whistle repertoire. We developed a computational model of nightingale whistle-matching
behavior that revealed a hierarchical organization of acoustic feature production.
During whistle matching, the feature integration process is constrained by the
duration of syllables, and pitch matching follows within this temporal framework,
forcing a trade-off between the two features. Our findings reveal a complex interplay
between the spectral and temporal domains that shapes song-matching behavior.
acknowledgement: 'We would like to thank J. Benichov and N. Hein for their help with
fieldwork; M. Ramadas for helping with the segmentation analysis; T. Eliav, C. Chintaluri,
G. Tkacik, and A. Navas for providing helpful comments to the project and manuscript;
and A. Costalunga for the drawings of nightingales. Funding sources: The Joachim
Herz Stiftung Add-on Fellowships for Interdisciplinary Life Science, awarded to
G.C.; the ERC Consolidator Grant 819603 SYNAPSEEK, awarded to T.P.V.; and DFG Research
Unit 5768–532521431, DFG Research Grant-547921981, DFG SFB 1315–327654276, and the
ERC Starting Grant 757459 MIDNIGHT, awarded to D.V.'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Juan Sebastian
full_name: Calderon Garcia, Juan Sebastian
id: 1271b54b-dbcd-11ea-9d1d-d92da838fe2c
last_name: Calderon Garcia
- first_name: Giacomo
full_name: Costalunga, Giacomo
last_name: Costalunga
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Daniela
full_name: Vallentin, Daniela
last_name: Vallentin
citation:
ama: Calderon Garcia JS, Costalunga G, Vogels TP, Vallentin D. Interplay between
syllable duration and pitch during whistle matching in wild nightingales. Current
Biology. 2026. doi:10.1016/j.cub.2025.12.025
apa: Calderon Garcia, J. S., Costalunga, G., Vogels, T. P., & Vallentin, D.
(2026). Interplay between syllable duration and pitch during whistle matching
in wild nightingales. Current Biology. Elsevier. https://doi.org/10.1016/j.cub.2025.12.025
chicago: Calderon Garcia, Juan Sebastian, Giacomo Costalunga, Tim P Vogels, and
Daniela Vallentin. “Interplay between Syllable Duration and Pitch during Whistle
Matching in Wild Nightingales.” Current Biology. Elsevier, 2026. https://doi.org/10.1016/j.cub.2025.12.025.
ieee: J. S. Calderon Garcia, G. Costalunga, T. P. Vogels, and D. Vallentin, “Interplay
between syllable duration and pitch during whistle matching in wild nightingales,”
Current Biology. Elsevier, 2026.
ista: Calderon Garcia JS, Costalunga G, Vogels TP, Vallentin D. 2026. Interplay
between syllable duration and pitch during whistle matching in wild nightingales.
Current Biology.
mla: Calderon Garcia, Juan Sebastian, et al. “Interplay between Syllable Duration
and Pitch during Whistle Matching in Wild Nightingales.” Current Biology,
Elsevier, 2026, doi:10.1016/j.cub.2025.12.025.
short: J.S. Calderon Garcia, G. Costalunga, T.P. Vogels, D. Vallentin, Current Biology
(2026).
date_created: 2026-01-14T12:00:29Z
date_published: 2026-01-12T00:00:00Z
date_updated: 2026-01-20T07:33:32Z
day: '12'
ddc:
- '570'
- '577'
department:
- _id: GradSch
- _id: TiVo
doi: 10.1016/j.cub.2025.12.025
ec_funded: 1
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.cub.2025.12.025
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Current Biology
publication_identifier:
eissn:
- 1879-0445
issn:
- 0960-9822
publication_status: epub_ahead
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Interplay between syllable duration and pitch during whistle matching in wild
nightingales
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '21378'
abstract:
- lang: eng
text: From insects to mammals, essential brain functions, such as forming long-term
memories (LTMs), increase metabolic activity in stimulated neurons to meet the
energetic demand associated with brain activation. However, while impairing neuronal
metabolism limits brain performance, whether expanding the metabolic capacity
of neurons boosts brain function remains poorly understood. Here, we show that
LTM formation of flies and mice can be enhanced by increasing mitochondrial metabolism
in central memory circuits. By knocking down the mitochondrial Ca2+ exporter Letm1,
we favour Ca2+ retention in the mitochondrial matrix of neurons due to reduction
of mitochondrial H+/Ca2+ exchange. The resulting increase in mitochondrial Ca2+
over-activates mitochondrial metabolism in neurons of central memory circuits,
leading to improved LTM storage in training paradigms in which wild-type counterparts
of both species fail to remember. Our findings unveil an evolutionarily conserved
mechanism that controls mitochondrial metabolism in neurons and indicate its involvement
in shaping higher brain functions, such as LTM.
acknowledgement: We thank all members of the laboratory of J.d.J.-S. for insightful
discussions and comments. We thank S. Perez for technical assistance. This work
was made possible by the Paris Brain Institute Diane Barriere Chair in Synaptic
Bioenergetics awarded to J.d.J.-S., who is also supported by an ERC Starting Grant
(SynaptoEnergy, European Research Council; ERC-StG-852873), 2019 ATIP-Avenir Grant
(CNRS, Inserm), a Big Brain Theory Grant (ICM Foundation) and a Kavli Exploratory
Award (Kavli Foundation). This work was also supported by an ERC Advanced Grant
(EnergyMeMo; ERC-AdG-741550) to T.P. and grants from the Agence Nationale de la
Recherche to P.Y.P. (ANR-20-CE92-0047-01), T.P. (ANR-23-CE16-0029-01), A.P. and
J.d.J.-S. (ANR-22-CE16-0020) and J.d.J.-S. (ANR-24-CE16-0221). T.P., P.Y.P. and
J.d.J.-S. are permanent CNRS researchers. A.P. is a permanent ESPCI associate professor.
T.C. was funded by the French Ministry of Research and the Fondation pour la Recherche
Médicale. V.R. was funded by the Max Planck Society, the Chan Zuckerberg Initiative
DAF, an advised fund of the Silicon Valley Community Foundation grant number 2024-349543
and the NIH Director’s New Innovator Award (DP2 MH140148). A.B.-G. and C.R.-D. received
funding from an ERC Starting Grant (HighMemory; ERC-StG-948217), the Ministry of
Economy and Competitiveness (PID2021-122795OB-I00) and the Departament d’Economia
i Coneixement de la Generalitat de Catalunya (SGR 00022). T.P.V. was funded by the
Wellcome Trust and a Royal Society Sir Henry Dale Research Fellowship (WT100000)
and a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z). K.G. was supported
by the DIM C-BRAINS, funded by the Conseil Régional d’Ile-de-France. The contributions
of H.F. and E.R.S. were supported by the Howard Hughes Medical Institute. The PHENO-ICMice
animal Core at ICM is supported by two ‘Investissements d’avenir’ (ANR-10- IAIHU-06
and ANR-11-INBS-0011-NeurATRIS) and the Fondation pour la Recherche Médicale.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Anjali
full_name: Amrapali Vishwanath, Anjali
last_name: Amrapali Vishwanath
- first_name: Typhaine
full_name: Comyn, Typhaine
last_name: Comyn
- first_name: Rodrigo G.
full_name: Mira, Rodrigo G.
last_name: Mira
- first_name: Claire
full_name: Brossier, Claire
last_name: Brossier
- first_name: Carlos
full_name: Pascual-Caro, Carlos
last_name: Pascual-Caro
- first_name: Maya
full_name: Faour, Maya
last_name: Faour
- first_name: Kahina
full_name: Boumendil, Kahina
last_name: Boumendil
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: BA06AFEE-A4BA-11EA-AE5C-14673DDC885E
last_name: Chintaluri
orcid: 0000-0003-4252-1608
- first_name: Carla
full_name: Ramon-Duaso, Carla
last_name: Ramon-Duaso
- first_name: Ruolin
full_name: Fan, Ruolin
last_name: Fan
- first_name: Kishalay
full_name: Ghosh, Kishalay
last_name: Ghosh
- first_name: Helen
full_name: Farrants, Helen
last_name: Farrants
- first_name: Jean-Paul
full_name: Berwick, Jean-Paul
last_name: Berwick
- first_name: Riya
full_name: Sivakumar, Riya
last_name: Sivakumar
- first_name: Mario
full_name: Lopez-Manzaneda, Mario
last_name: Lopez-Manzaneda
- first_name: Eric R.
full_name: Schreiter, Eric R.
last_name: Schreiter
- first_name: Thomas
full_name: Preat, Thomas
last_name: Preat
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Vidhya
full_name: Rangaraju, Vidhya
last_name: Rangaraju
- first_name: Arnau
full_name: Busquets-Garcia, Arnau
last_name: Busquets-Garcia
- first_name: Pierre-Yves
full_name: Plaçais, Pierre-Yves
last_name: Plaçais
- first_name: Alice
full_name: Pavlowsky, Alice
last_name: Pavlowsky
- first_name: Jaime
full_name: de Juan-Sanz, Jaime
last_name: de Juan-Sanz
citation:
ama: Amrapali Vishwanath A, Comyn T, Mira RG, et al. Mitochondrial Ca2+ efflux controls
neuronal metabolism and long-term memory across species. Nature Metabolism.
2026;8(2):467-488. doi:10.1038/s42255-026-01451-w
apa: Amrapali Vishwanath, A., Comyn, T., Mira, R. G., Brossier, C., Pascual-Caro,
C., Faour, M., … de Juan-Sanz, J. (2026). Mitochondrial Ca2+ efflux controls neuronal
metabolism and long-term memory across species. Nature Metabolism. Springer
Nature. https://doi.org/10.1038/s42255-026-01451-w
chicago: Amrapali Vishwanath, Anjali, Typhaine Comyn, Rodrigo G. Mira, Claire Brossier,
Carlos Pascual-Caro, Maya Faour, Kahina Boumendil, et al. “Mitochondrial Ca2+
Efflux Controls Neuronal Metabolism and Long-Term Memory across Species.” Nature
Metabolism. Springer Nature, 2026. https://doi.org/10.1038/s42255-026-01451-w.
ieee: A. Amrapali Vishwanath et al., “Mitochondrial Ca2+ efflux controls
neuronal metabolism and long-term memory across species,” Nature Metabolism,
vol. 8, no. 2. Springer Nature, pp. 467–488, 2026.
ista: Amrapali Vishwanath A, Comyn T, Mira RG, Brossier C, Pascual-Caro C, Faour
M, Boumendil K, Chintaluri C, Ramon-Duaso C, Fan R, Ghosh K, Farrants H, Berwick
J-P, Sivakumar R, Lopez-Manzaneda M, Schreiter ER, Preat T, Vogels TP, Rangaraju
V, Busquets-Garcia A, Plaçais P-Y, Pavlowsky A, de Juan-Sanz J. 2026. Mitochondrial
Ca2+ efflux controls neuronal metabolism and long-term memory across species.
Nature Metabolism. 8(2), 467–488.
mla: Amrapali Vishwanath, Anjali, et al. “Mitochondrial Ca2+ Efflux Controls Neuronal
Metabolism and Long-Term Memory across Species.” Nature Metabolism, vol.
8, no. 2, Springer Nature, 2026, pp. 467–88, doi:10.1038/s42255-026-01451-w.
short: A. Amrapali Vishwanath, T. Comyn, R.G. Mira, C. Brossier, C. Pascual-Caro,
M. Faour, K. Boumendil, C. Chintaluri, C. Ramon-Duaso, R. Fan, K. Ghosh, H. Farrants,
J.-P. Berwick, R. Sivakumar, M. Lopez-Manzaneda, E.R. Schreiter, T. Preat, T.P.
Vogels, V. Rangaraju, A. Busquets-Garcia, P.-Y. Plaçais, A. Pavlowsky, J. de Juan-Sanz,
Nature Metabolism 8 (2026) 467–488.
date_created: 2026-03-02T10:04:49Z
date_published: 2026-02-11T00:00:00Z
date_updated: 2026-03-02T15:23:10Z
day: '11'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s42255-026-01451-w
external_id:
pmid:
- '41673453'
file:
- access_level: open_access
checksum: 365932a599d05bc9ce8a57204e7a1465
content_type: application/pdf
creator: dernst
date_created: 2026-03-02T15:21:27Z
date_updated: 2026-03-02T15:21:27Z
file_id: '21392'
file_name: 2026_NatureMetab_AmrapaliVishwanath.pdf
file_size: 5326608
relation: main_file
success: 1
file_date_updated: 2026-03-02T15:21:27Z
has_accepted_license: '1'
intvolume: ' 8'
issue: '2'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: 467-488
pmid: 1
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Nature Metabolism
publication_identifier:
eissn:
- 2522-5812
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory
across species
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2026'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '19640'
abstract:
- lang: eng
text: Synaptic plasticity is a key player in the brain’s life-long learning abilities.
However, due to experimental limitations, the mechanistic link between synaptic
plasticity rules and the network-level computations they enable remain opaque.
Here we use evolutionary strategies (ES) to meta learn local co-active plasticity
rules in large recurrent spiking networks with excitatory (E) and inhibitory (I)
neurons, using parameterizations of increasing complexity. We discover rules that
robustly stabilize network dynamics for all four synapse types acting in isolation
(E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity
detection can also be included in the search constraints. However, our meta learning
strategy begins to fail for co-active rules of increasing complexity, as it is
challenging to devise loss functions that effectively constrain network dynamics
to plausible solutions a priori. Moreover, in line with previous work, we can
find multiple degenerate solutions with identical network behaviour. As a local
optimization strategy, ES provides one solution at a time and makes exploration
of this degeneracy cumbersome. Regardless, we can glean the interdependecies of
various plasticity parameters by considering the covariance matrix learned alongside
the optimal rule with ES. Our work provides a proof of principle for the success
of machine-learning-guided discovery of plasticity rules in large spiking networks,
and points at the necessity of more elaborate search strategies going forward.
acknowledgement: We would like to thank Chaitanya Chintaluri, Nicoleta Condruz and
Douglas Feitosa Tomé for insightful discussions.
article_number: e1012910
article_processing_charge: Yes
article_type: original
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
- first_name: Henning
full_name: Sprekeler, Henning
last_name: Sprekeler
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. Balancing complexity,
performance and plausibility to meta learn plasticity rules in recurrent spiking
networks. PLoS Computational Biology. 2025;21(4). doi:10.1371/journal.pcbi.1012910
apa: Confavreux, B. J., Agnes, E. J., Zenke, F., Sprekeler, H., & Vogels, T.
P. (2025). Balancing complexity, performance and plausibility to meta learn plasticity
rules in recurrent spiking networks. PLoS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1012910
chicago: Confavreux, Basile J, Everton J. Agnes, Friedemann Zenke, Henning Sprekeler,
and Tim P Vogels. “Balancing Complexity, Performance and Plausibility to Meta
Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational Biology.
Public Library of Science, 2025. https://doi.org/10.1371/journal.pcbi.1012910.
ieee: B. J. Confavreux, E. J. Agnes, F. Zenke, H. Sprekeler, and T. P. Vogels, “Balancing
complexity, performance and plausibility to meta learn plasticity rules in recurrent
spiking networks,” PLoS Computational Biology, vol. 21, no. 4. Public Library
of Science, 2025.
ista: Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. 2025. Balancing
complexity, performance and plausibility to meta learn plasticity rules in recurrent
spiking networks. PLoS Computational Biology. 21(4), e1012910.
mla: Confavreux, Basile J., et al. “Balancing Complexity, Performance and Plausibility
to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational
Biology, vol. 21, no. 4, e1012910, Public Library of Science, 2025, doi:10.1371/journal.pcbi.1012910.
short: B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational
Biology 21 (2025).
corr_author: '1'
date_created: 2025-05-04T22:02:31Z
date_published: 2025-04-24T00:00:00Z
date_updated: 2025-09-30T12:22:33Z
day: '24'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1012910
external_id:
isi:
- '001474257000002'
pmid:
- '40273284 '
file:
- access_level: open_access
checksum: 6437a1aab52813ab7e310e3b4fb36e3b
content_type: application/pdf
creator: dernst
date_created: 2025-05-05T11:17:49Z
date_updated: 2025-05-05T11:17:49Z
file_id: '19654'
file_name: 2025_PLoSCompBio_Confavreux.pdf
file_size: 9771636
relation: main_file
success: 1
file_date_updated: 2025-05-05T11:17:49Z
has_accepted_license: '1'
intvolume: ' 21'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/VogelsLab/SpikES
scopus_import: '1'
status: public
title: Balancing complexity, performance and plausibility to meta learn plasticity
rules in recurrent spiking networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 21
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '8125'
abstract:
- lang: eng
text: "Biological memory is known to be flexible—memory formation and recall depend
on factors such as the behavioral context of the organism. However, this property
is often ignored in associative memory models, leaving it unclear how memories
can be organized and recalled when subject to contextual control. Because of the
lack of a rigorous analytical framework, it is also unknown how contextual control
affects memory stability, storage capacity, and information content. Here, we
bring the dynamic nature of memory to the fore by introducing a novel model of
associative memory, which we refer to as the context-modular memory network. In
our model, stored memory patterns are associated to one of several background
network states, or contexts. Memories are accessible when their corresponding
context is active, and are otherwise inaccessible. Context modulates the effective
network connectivity by imposing a specific\r\nconfiguration of neuronal and synaptic
gating—gated neurons (synapses) have their activity (weights) momentarily silenced,
thereby reducing interference from memories belonging to other contexts. Memory
patterns are randomly and independently chosen, while neuronal and synaptic gates
may be selected randomly or optimized through a process of contextual synaptic
refinement. Through analytic and numerical results, we show that context-modular
memory networks can exhibit both improved memory capacity and differential control
of memory stability with random gating (especially for neuronal gating). For contextual
synaptic refinement, we devise a method in which synapses are gated off for a
given context if they destabilize the memory patterns in that context, drastically
improving memory capacity and enabling even more precise control over memory stability.
Notably, synaptic refinement allows for patterns to be\r\naccessible in multiple
contexts, stabilizing memory patterns even for weight matrices that alone do not
contain any information about the memory patterns, such as Gaussian random matrices.
Overall, our model integrates recent ideas about context-dependent memory organization
with classic associative memory models and proposes a rigorous theory which can
act as a framework for future work. Furthermore, our work carries important implications
for the understanding of biological memory storage and recall in the brain, such
as highlighting an intriguing trade-off between memory capacity and accessibility."
acknowledgement: "We thank Helen Barron, Vezha Boboeva, Adam Packer, João Sacramento,
Andrew Saxe, Misha Tsodyks, and Friedemann Zenke for helpful comments at various
stages of this work, and Rubem Erichsen, Jr. for carefully reading the manuscript
and valuable comments. This work was\r\nsupported by a Sir Henry Dale Fellowship
by the Wellcome Trust and the Royal Society [No. WT100000 (W. F. P., E. J. A., and
T. P. V.)], a Wellcome Trust Senior Research Fellowship [No. 214316/Z/18/Z (E. J.
A. and T. P. V.)], and a Research Project Grant by the Leverhulme Trust\r\n[No.
RPG-2016-446 (E. J. A.)]. "
article_number: '011057'
article_processing_charge: Yes
article_type: original
author:
- first_name: William F.
full_name: Podlaski, William F.
last_name: Podlaski
orcid: 0000-0001-6619-7502
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
orcid: 0000-0001-7184-7311
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in
associative memory networks with context-dependent neuronal and synaptic gating.
Physical Review X. 2025;15. doi:10.1103/PhysRevX.15.011057
apa: Podlaski, W. F., Agnes, E. J., & Vogels, T. P. (2025). High capacity and
dynamic accessibility in associative memory networks with context-dependent neuronal
and synaptic gating. Physical Review X. American Physical Society. https://doi.org/10.1103/PhysRevX.15.011057
chicago: Podlaski, William F., Everton J. Agnes, and Tim P Vogels. “High Capacity
and Dynamic Accessibility in Associative Memory Networks with Context-Dependent
Neuronal and Synaptic Gating.” Physical Review X. American Physical Society,
2025. https://doi.org/10.1103/PhysRevX.15.011057.
ieee: W. F. Podlaski, E. J. Agnes, and T. P. Vogels, “High capacity and dynamic
accessibility in associative memory networks with context-dependent neuronal and
synaptic gating,” Physical Review X, vol. 15. American Physical Society,
2025.
ista: Podlaski WF, Agnes EJ, Vogels TP. 2025. High capacity and dynamic accessibility
in associative memory networks with context-dependent neuronal and synaptic gating.
Physical Review X. 15, 011057.
mla: Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative
Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” Physical
Review X, vol. 15, 011057, American Physical Society, 2025, doi:10.1103/PhysRevX.15.011057.
short: W.F. Podlaski, E.J. Agnes, T.P. Vogels, Physical Review X 15 (2025).
corr_author: '1'
date_created: 2020-07-16T12:24:28Z
date_published: 2025-03-13T00:00:00Z
date_updated: 2025-05-19T13:51:27Z
day: '13'
ddc:
- '530'
department:
- _id: TiVo
doi: 10.1103/PhysRevX.15.011057
external_id:
isi:
- '001451378900002'
file:
- access_level: open_access
checksum: 1f27ee469ab51a3e1ce1e2df0022e81d
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creator: dernst
date_created: 2025-03-20T12:47:17Z
date_updated: 2025-03-20T12:47:17Z
file_id: '19432'
file_name: 2025_PhysReviewX_Podlaski.pdf
file_size: 1373704
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success: 1
file_date_updated: 2025-03-20T12:47:17Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
language:
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locked: '1'
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Physical Review X
publication_identifier:
eissn:
- 2160-3308
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/wpodlaski/contextual-memory-nets
scopus_import: '1'
status: public
title: High capacity and dynamic accessibility in associative memory networks with
context-dependent neuronal and synaptic gating
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 15
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '14841'
abstract:
- lang: eng
text: De novo heterozygous variants in KCNC2 encoding the voltage-gated potassium
(K+) channel subunit Kv3.2 are a recently described cause of developmental and
epileptic encephalopathy (DEE). A de novo variant in KCNC2 c.374G > A (p.Cys125Tyr)
was identified via exome sequencing in a patient with DEE. Relative to wild-type
Kv3.2, Kv3.2-p.Cys125Tyr induces K+ currents exhibiting a large hyperpolarizing
shift in the voltage dependence of activation, accelerated activation, and delayed
deactivation consistent with a relative stabilization of the open conformation,
along with increased current density. Leveraging the cryogenic electron microscopy
(cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong
π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix
of the T1 domain promotes a relative stabilization of the open conformation of
the channel, which underlies the observed gain of function. A multicompartment
computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking
γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the
Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition
in cerebral cortex circuits to explain the resulting epilepsy.
acknowledgement: This work was supported by an ERC Consolidator Grant (SYNAPSEEK)
to T.P.V., the NOMIS Foundation through the NOMIS Fellowships program at IST Austria
to C.B.C., a Jefferson Synaptic Biology Center Pilot Project Grant to M.C., NIH
NINDS U54 NS108874 (PI, Alfred L. George), and NIH NINDS R01 NS122887 to E.M.G.
The computations were enabled by resources provided by the Swedish National Infrastructure
for Computing (SNIC) at the PDC Center for High-Performance Computing, KTH Royal
Institute of Technology, partially funded by the Swedish Research Council through
grant agreement no. 2018-05973. We thank Akshay Sridhar for the fruitful discussion
of the project.
article_number: e2307776121
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jerome
full_name: Clatot, Jerome
last_name: Clatot
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Qiansheng
full_name: Liang, Qiansheng
last_name: Liang
- first_name: Tanadet
full_name: Pipatpolkai, Tanadet
last_name: Pipatpolkai
- first_name: Shavonne L.
full_name: Massey, Shavonne L.
last_name: Massey
- first_name: Ingo
full_name: Helbig, Ingo
last_name: Helbig
- first_name: Lucie
full_name: Delemotte, Lucie
last_name: Delemotte
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Manuel
full_name: Covarrubias, Manuel
last_name: Covarrubias
- first_name: Ethan M.
full_name: Goldberg, Ethan M.
last_name: Goldberg
citation:
ama: Clatot J, Currin C, Liang Q, et al. A structurally precise mechanism links
an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction.
Proceedings of the National Academy of Sciences of the United States of America.
2024;121(3). doi:10.1073/pnas.2307776121
apa: Clatot, J., Currin, C., Liang, Q., Pipatpolkai, T., Massey, S. L., Helbig,
I., … Goldberg, E. M. (2024). A structurally precise mechanism links an epilepsy-associated
KCNC2 potassium channel mutation to interneuron dysfunction. Proceedings of
the National Academy of Sciences of the United States of America. National
Academy of Sciences. https://doi.org/10.1073/pnas.2307776121
chicago: Clatot, Jerome, Christopher Currin, Qiansheng Liang, Tanadet Pipatpolkai,
Shavonne L. Massey, Ingo Helbig, Lucie Delemotte, Tim P Vogels, Manuel Covarrubias,
and Ethan M. Goldberg. “A Structurally Precise Mechanism Links an Epilepsy-Associated
KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of
the National Academy of Sciences of the United States of America. National
Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2307776121.
ieee: J. Clatot et al., “A structurally precise mechanism links an epilepsy-associated
KCNC2 potassium channel mutation to interneuron dysfunction,” Proceedings of
the National Academy of Sciences of the United States of America, vol. 121,
no. 3. National Academy of Sciences, 2024.
ista: Clatot J, Currin C, Liang Q, Pipatpolkai T, Massey SL, Helbig I, Delemotte
L, Vogels TP, Covarrubias M, Goldberg EM. 2024. A structurally precise mechanism
links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction.
Proceedings of the National Academy of Sciences of the United States of America.
121(3), e2307776121.
mla: Clatot, Jerome, et al. “A Structurally Precise Mechanism Links an Epilepsy-Associated
KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of
the National Academy of Sciences of the United States of America, vol. 121,
no. 3, e2307776121, National Academy of Sciences, 2024, doi:10.1073/pnas.2307776121.
short: J. Clatot, C. Currin, Q. Liang, T. Pipatpolkai, S.L. Massey, I. Helbig, L.
Delemotte, T.P. Vogels, M. Covarrubias, E.M. Goldberg, Proceedings of the National
Academy of Sciences of the United States of America 121 (2024).
date_created: 2024-01-21T23:00:56Z
date_published: 2024-01-16T00:00:00Z
date_updated: 2025-09-04T11:47:47Z
day: '16'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1073/pnas.2307776121
ec_funded: 1
external_id:
isi:
- '001167401000001'
pmid:
- '38194456'
file:
- access_level: open_access
checksum: f498c643be81895dd3a69ee90115a782
content_type: application/pdf
creator: dernst
date_created: 2025-04-23T13:51:16Z
date_updated: 2025-04-23T13:51:16Z
file_id: '19613'
file_name: 2024_PNAS_Clatot.pdf
file_size: 3060109
relation: main_file
success: 1
file_date_updated: 2025-04-23T13:51:16Z
has_accepted_license: '1'
intvolume: ' 121'
isi: 1
issue: '3'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: 'https://github.com/ChrisCurrin/pv-kcnc2 '
scopus_import: '1'
status: public
title: A structurally precise mechanism links an epilepsy-associated KCNC2 potassium
channel mutation to interneuron dysfunction
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 121
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '15171'
abstract:
- lang: eng
text: The brain’s functionality is developed and maintained through synaptic plasticity.
As synapses undergo plasticity, they also affect each other. The nature of such
‘co-dependency’ is difficult to disentangle experimentally, because multiple synapses
must be monitored simultaneously. To help understand the experimentally observed
phenomena, we introduce a framework that formalizes synaptic co-dependency between
different connection types. The resulting model explains how inhibition can gate
excitatory plasticity while neighboring excitatory–excitatory interactions determine
the strength of long-term potentiation. Furthermore, we show how the interplay
between excitatory and inhibitory synapses can account for the quick rise and
long-term stability of a variety of synaptic weight profiles, such as orientation
tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks,
co-dependent plasticity produces rich and stable motor cortex-like dynamics with
high input sensitivity. Our results suggest an essential role for the neighborly
synaptic interaction during learning, connecting micro-level physiology with network-wide
phenomena.
acknowledgement: We thank C. Currin, B. Podlaski and the members of the Vogels group
for fruitful discussions. E.J.A. and T.P.V. were supported by a Research Project
Grant from the Leverhulme Trust (RPG-2016-446; TPV), a Sir Henry Dale Fellowship
from the Wellcome Trust and the Royal Society (WT100000; T.P.V.), a Wellcome Trust
Senior Research Fellowship (214316/Z/18/Z; T.P.V.) and a European Research Council
Consolidator Grant (SYNAPSEEK, 819603; T.P.V.). For the purpose of open access,
the authors have applied a CC BY public copyright license to any author accepted
manuscript version arising from this submission. Open access funding provided by
University of Basel.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Agnes EJ, Vogels TP. Co-dependent excitatory and inhibitory plasticity accounts
for quick, stable and long-lasting memories in biological networks. Nature
Neuroscience. 2024;27:964-974. doi:10.1038/s41593-024-01597-4
apa: Agnes, E. J., & Vogels, T. P. (2024). Co-dependent excitatory and inhibitory
plasticity accounts for quick, stable and long-lasting memories in biological
networks. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-024-01597-4
chicago: Agnes, Everton J., and Tim P Vogels. “Co-Dependent Excitatory and Inhibitory
Plasticity Accounts for Quick, Stable and Long-Lasting Memories in Biological
Networks.” Nature Neuroscience. Springer Nature, 2024. https://doi.org/10.1038/s41593-024-01597-4.
ieee: E. J. Agnes and T. P. Vogels, “Co-dependent excitatory and inhibitory plasticity
accounts for quick, stable and long-lasting memories in biological networks,”
Nature Neuroscience, vol. 27. Springer Nature, pp. 964–974, 2024.
ista: Agnes EJ, Vogels TP. 2024. Co-dependent excitatory and inhibitory plasticity
accounts for quick, stable and long-lasting memories in biological networks. Nature
Neuroscience. 27, 964–974.
mla: Agnes, Everton J., and Tim P. Vogels. “Co-Dependent Excitatory and Inhibitory
Plasticity Accounts for Quick, Stable and Long-Lasting Memories in Biological
Networks.” Nature Neuroscience, vol. 27, Springer Nature, 2024, pp. 964–74,
doi:10.1038/s41593-024-01597-4.
short: E.J. Agnes, T.P. Vogels, Nature Neuroscience 27 (2024) 964–974.
date_created: 2024-03-24T23:01:00Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2025-09-04T13:06:06Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s41593-024-01597-4
ec_funded: 1
external_id:
isi:
- '001190081400001'
pmid:
- '38509348 '
file:
- access_level: open_access
checksum: dfca68a24749575b912b3a78a7de4516
content_type: application/pdf
creator: dernst
date_created: 2025-06-25T08:45:32Z
date_updated: 2025-06-25T08:45:32Z
file_id: '19902'
file_name: 2025_NatureNeuroscience_Agnes.pdf
file_size: 10508018
relation: main_file
success: 1
file_date_updated: 2025-06-25T08:45:32Z
has_accepted_license: '1'
intvolume: ' 27'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 964-974
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Co-dependent excitatory and inhibitory plasticity accounts for quick, stable
and long-lasting memories in biological networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 27
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '14666'
abstract:
- lang: eng
text: So-called spontaneous activity is a central hallmark of most nervous systems.
Such non-causal firing is contrary to the tenet of spikes as a means of communication,
and its purpose remains unclear. We propose that self-initiated firing can serve
as a release valve to protect neurons from the toxic conditions arising in mitochondria
from lower-than-baseline energy consumption. To demonstrate the viability of our
hypothesis, we built a set of models that incorporate recent experimental results
indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP),
adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing.
We explore the relationship of metabolic cost of spiking with its effect on the
temporal patterning of spikes and reproduce experimentally observed changes in
intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with
ROS-modulated potassium channels. We also show that metabolic spiking homeostasis
can produce indefinitely sustained avalanche dynamics in cortical circuits. Our
theory can account for key features of neuronal activity observed in many studies
ranging from ion channel function all the way to resting state dynamics. We finish
with a set of experimental predictions that would confirm an integrated, crucial
role for metabolically regulated spiking and firmly link metabolic homeostasis
and neuronal function.
acknowledgement: We thank Prof. C. Nazaret and Prof. J.-P. Mazat for sharing the code
of their mitochondrial model. We also thank G. Miesenböck, E. Marder, L. Abbott,
A. Kempf, P. Hasenhuetl, W. Podlaski, F. Zenke, E. Agnes, P. Bozelos, J. Watson,
B. Confavreux, and G. Christodoulou, and the rest of the Vogels Lab for their feedback.
This work was funded by Wellcome Trust and Royal Society Sir Henry Dale Research
Fellowship (WT100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z),
and a UK Research and Innovation, Biotechnology and Biological Sciences Research
Council grant (UKRI-BBSRC BB/N019512/1).
article_number: e2306525120
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: E4EDB536-3485-11EA-98D2-20AF3DDC885E
last_name: Chintaluri
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Chintaluri C, Vogels TP. Metabolically regulated spiking could serve neuronal
energy homeostasis and protect from reactive oxygen species. Proceedings of
the National Academy of Sciences of the United States of America. 2023;120(48).
doi:10.1073/pnas.2306525120
apa: Chintaluri, C., & Vogels, T. P. (2023). Metabolically regulated spiking
could serve neuronal energy homeostasis and protect from reactive oxygen species.
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences. https://doi.org/10.1073/pnas.2306525120
chicago: Chintaluri, Chaitanya, and Tim P Vogels. “Metabolically Regulated Spiking
Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.”
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2306525120.
ieee: C. Chintaluri and T. P. Vogels, “Metabolically regulated spiking could serve
neuronal energy homeostasis and protect from reactive oxygen species,” Proceedings
of the National Academy of Sciences of the United States of America, vol.
120, no. 48. National Academy of Sciences, 2023.
ista: Chintaluri C, Vogels TP. 2023. Metabolically regulated spiking could serve
neuronal energy homeostasis and protect from reactive oxygen species. Proceedings
of the National Academy of Sciences of the United States of America. 120(48),
e2306525120.
mla: Chintaluri, Chaitanya, and Tim P. Vogels. “Metabolically Regulated Spiking
Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.”
Proceedings of the National Academy of Sciences of the United States of America,
vol. 120, no. 48, e2306525120, National Academy of Sciences, 2023, doi:10.1073/pnas.2306525120.
short: C. Chintaluri, T.P. Vogels, Proceedings of the National Academy of Sciences
of the United States of America 120 (2023).
corr_author: '1'
date_created: 2023-12-10T23:01:00Z
date_published: 2023-11-21T00:00:00Z
date_updated: 2025-09-24T11:16:56Z
day: '21'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1073/pnas.2306525120
external_id:
isi:
- '001157389000005'
pmid:
- '37988463'
file:
- access_level: open_access
checksum: bf4ec38602a70dae4338077a5a4d497f
content_type: application/pdf
creator: dernst
date_created: 2023-12-11T12:45:12Z
date_updated: 2023-12-11T12:45:12Z
file_id: '14678'
file_name: 2023_PNAS_Chintaluri.pdf
file_size: 16891602
relation: main_file
success: 1
file_date_updated: 2023-12-11T12:45:12Z
has_accepted_license: '1'
intvolume: ' 120'
isi: 1
issue: '48'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ccluri/metabolic_spiking
scopus_import: '1'
status: public
title: Metabolically regulated spiking could serve neuronal energy homeostasis and
protect from reactive oxygen species
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 120
year: '2023'
...
---
_id: '12009'
abstract:
- lang: eng
text: Changes in the short-term dynamics of excitatory synapses over development
have been observed throughout cortex, but their purpose and consequences remain
unclear. Here, we propose that developmental changes in synaptic dynamics buffer
the effect of slow inhibitory long-term plasticity, allowing for continuously
stable neural activity. Using computational modeling we demonstrate that early
in development excitatory short-term depression quickly stabilises neural activity,
even in the face of strong, unbalanced excitation. We introduce a model of the
commonly observed developmental shift from depression to facilitation and show
that neural activity remains stable throughout development, while inhibitory synaptic
plasticity slowly balances excitation, consistent with experimental observations.
Our model predicts changes in the input responses from phasic to phasic-and-tonic
and more precise spike timings. We also observe a gradual emergence of short-lasting
memory traces governed by short-term plasticity development. We conclude that
the developmental depression-to-facilitation shift may control excitation-inhibition
balance throughout development with important functional consequences.
acknowledgement: We would like to thank the Vogels Lab for feedback on an earlier
version of this manuscript. D.W.J. was supported by a Marshall Scholarship and a
Clarendon Scholarship. R.P.C. and T.P.V. were supported by a Wellcome Trust and
Royal Society Sir Henry Dale Fellowship (WT 100000), a Wellcome Trust Senior Research
Fellowship (214316/Z/18/Z), and an ERC Consolidator Grant (SYNAPSEEK).
article_number: '873'
article_processing_charge: No
article_type: original
author:
- first_name: David W.
full_name: Jia, David W.
last_name: Jia
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Rui Ponte
full_name: Costa, Rui Ponte
last_name: Costa
citation:
ama: Jia DW, Vogels TP, Costa RP. Developmental depression-to-facilitation shift
controls excitation-inhibition balance. Communications biology. 2022;5.
doi:10.1038/s42003-022-03801-2
apa: Jia, D. W., Vogels, T. P., & Costa, R. P. (2022). Developmental depression-to-facilitation
shift controls excitation-inhibition balance. Communications Biology. Springer
Nature. https://doi.org/10.1038/s42003-022-03801-2
chicago: Jia, David W., Tim P Vogels, and Rui Ponte Costa. “Developmental Depression-to-Facilitation
Shift Controls Excitation-Inhibition Balance.” Communications Biology.
Springer Nature, 2022. https://doi.org/10.1038/s42003-022-03801-2.
ieee: D. W. Jia, T. P. Vogels, and R. P. Costa, “Developmental depression-to-facilitation
shift controls excitation-inhibition balance,” Communications biology,
vol. 5. Springer Nature, 2022.
ista: Jia DW, Vogels TP, Costa RP. 2022. Developmental depression-to-facilitation
shift controls excitation-inhibition balance. Communications biology. 5, 873.
mla: Jia, David W., et al. “Developmental Depression-to-Facilitation Shift Controls
Excitation-Inhibition Balance.” Communications Biology, vol. 5, 873, Springer
Nature, 2022, doi:10.1038/s42003-022-03801-2.
short: D.W. Jia, T.P. Vogels, R.P. Costa, Communications Biology 5 (2022).
date_created: 2022-09-04T22:02:02Z
date_published: 2022-08-25T00:00:00Z
date_updated: 2025-04-14T09:44:14Z
day: '25'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s42003-022-03801-2
ec_funded: 1
external_id:
isi:
- '000844814800007'
file:
- access_level: open_access
checksum: 3ec724c4f6d3440028c217305e32915f
content_type: application/pdf
creator: dernst
date_created: 2022-09-05T08:55:11Z
date_updated: 2022-09-05T08:55:11Z
file_id: '12022'
file_name: 2022_CommBiology_Jia.pdf
file_size: 2491191
relation: main_file
success: 1
file_date_updated: 2022-09-05T08:55:11Z
has_accepted_license: '1'
intvolume: ' 5'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: "Whatâ\x80\x99s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks."
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Communications biology
publication_identifier:
eissn:
- 2399-3642
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Developmental depression-to-facilitation shift controls excitation-inhibition
balance
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 5
year: '2022'
...
---
_id: '12084'
abstract:
- lang: eng
text: Neuronal networks encode information through patterns of activity that define
the networks’ function. The neurons’ activity relies on specific connectivity
structures, yet the link between structure and function is not fully understood.
Here, we tackle this structure-function problem with a new conceptual approach.
Instead of manipulating the connectivity directly, we focus on upper triangular
matrices, which represent the network dynamics in a given orthonormal basis obtained
by the Schur decomposition. This abstraction allows us to independently manipulate
the eigenspectrum and feedforward structures of a connectivity matrix. Using this
method, we describe a diverse repertoire of non-normal transient amplification,
and to complement the analysis of the dynamical regimes, we quantify the geometry
of output trajectories through the effective rank of both the eigenvector and
the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s
imaginary distribution leads to highly amplifying regimes in linear and long-lasting
dynamics in nonlinear networks. We also find a trade-off between amplification
and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space.
Networks that can amplify a large number of orthogonal initial conditions produce
neuronal trajectories that lie in the same subspace of the neuronal state-space.
Finally, we examine networks of excitatory and inhibitory neurons. We find that
the strength of global inhibition is directly linked with the amplitude of amplification,
such that weakening inhibitory weights also decreases amplification, and that
the eigenspectrum’s imaginary distribution grows with an increase in the ratio
between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths.
Consequently, the strength of global inhibition reveals itself as a strong signature
for amplification and a potential control mechanism to switch dynamical regimes.
Our results shed a light on how biological networks, i.e., networks constrained
by Dale’s law, may be optimised for specific dynamical regimes.
acknowledgement: 'We thank Friedemann Zenke for his comments, especially on the effect
of the self loops on the spectrum. We also thank Ken Miller and Bill Podlaski for
helpful comments. This research was funded by a Wellcome Trust and Royal Society
Henry Dale Research Fellowship (WT100000; TPV), a Wellcome Senior Research Fellowship
(214316/Z/18/Z; GC, EJA, and TPV), and a Research Project Grant by the Leverhulme
Trust (RPG-2016-446; EJA and TPV). '
article_number: e1010365
article_processing_charge: No
article_type: original
author:
- first_name: Georgia
full_name: Christodoulou, Georgia
last_name: Christodoulou
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
citation:
ama: Christodoulou G, Vogels TP, Agnes EJ. Regimes and mechanisms of transient amplification
in abstract and biological neural networks. PLoS Computational Biology.
2022;18(8). doi:10.1371/journal.pcbi.1010365
apa: Christodoulou, G., Vogels, T. P., & Agnes, E. J. (2022). Regimes and mechanisms
of transient amplification in abstract and biological neural networks. PLoS
Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010365
chicago: Christodoulou, Georgia, Tim P Vogels, and Everton J. Agnes. “Regimes and
Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.”
PLoS Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1010365.
ieee: G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of
transient amplification in abstract and biological neural networks,” PLoS Computational
Biology, vol. 18, no. 8. Public Library of Science, 2022.
ista: Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient
amplification in abstract and biological neural networks. PLoS Computational Biology.
18(8), e1010365.
mla: Christodoulou, Georgia, et al. “Regimes and Mechanisms of Transient Amplification
in Abstract and Biological Neural Networks.” PLoS Computational Biology,
vol. 18, no. 8, e1010365, Public Library of Science, 2022, doi:10.1371/journal.pcbi.1010365.
short: G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18
(2022).
corr_author: '1'
date_created: 2022-09-11T22:01:56Z
date_published: 2022-08-15T00:00:00Z
date_updated: 2025-06-11T13:51:21Z
day: '15'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1010365
external_id:
isi:
- '000937227700001'
pmid:
- '35969604'
file:
- access_level: open_access
checksum: 8a81ab29f837991ee0ea770817c4a50e
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T07:47:55Z
date_updated: 2022-09-12T07:47:55Z
file_id: '12090'
file_name: 2022_PLoSCompBio_Christodoulou.pdf
file_size: 2867337
relation: main_file
success: 1
file_date_updated: 2022-09-12T07:47:55Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '8'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: "Whatâ\x80\x99s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks."
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Regimes and mechanisms of transient amplification in abstract and biological
neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 18
year: '2022'
...
---
_id: '13239'
abstract:
- lang: eng
text: Brains are thought to engage in predictive learning - learning to predict
upcoming stimuli - to construct an internal model of their environment. This is
especially notable for spatial navigation, as first described by Tolman’s latent
learning tasks. However, predictive learning has also been observed in sensory
cortex, in settings unrelated to spatial navigation. Apart from normative frameworks
such as active inference or efficient coding, what could be the utility of learning
to predict the patterns of occurrence of correlated stimuli? Here we show that
prediction, and thereby the construction of an internal model of sequential stimuli,
can bootstrap the learning process of a working memory task in a recurrent neural
network. We implemented predictive learning alongside working memory match-tasks,
and networks emerged to solve the prediction task first by encoding information
across time to predict upcoming stimuli, and then eavesdropped on this solution
to solve the matching task. Eavesdropping was most beneficial when neural resources
were limited. Hence, predictive learning acts as a general neural mechanism to
learn to store sensory information that can later be essential for working memory
tasks.
acknowledgement: "The authors would like to thank members of the Vogels lab and Manohar
lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans
for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP
was supported by funding from the Biotechnology and Biological Sciences Research
Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator
Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X
and Leverhulme Grant RPG-2018-310."
article_processing_charge: No
author:
- first_name: Thijs L.
full_name: Van Der Plas, Thijs L.
last_name: Van Der Plas
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Sanjay G.
full_name: Manohar, Sanjay G.
last_name: Manohar
citation:
ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural
networks to learn complex working memory tasks. In: Proceedings of Machine
Learning Research. Vol 199. ML Research Press; 2022:518-531.'
apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive
learning enables neural networks to learn complex working memory tasks. In Proceedings
of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.
chicago: Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive
Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings
of Machine Learning Research, 199:518–31. ML Research Press, 2022.
ieee: T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning
enables neural networks to learn complex working memory tasks,” in Proceedings
of Machine Learning Research, 2022, vol. 199, pp. 518–531.
ista: Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables
neural networks to learn complex working memory tasks. Proceedings of Machine
Learning Research. vol. 199, 518–531.
mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks
to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research,
vol. 199, ML Research Press, 2022, pp. 518–31.
short: T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine
Learning Research, ML Research Press, 2022, pp. 518–531.
date_created: 2023-07-16T22:01:12Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2025-04-14T07:54:31Z
day: '01'
ddc:
- '000'
department:
- _id: TiVo
ec_funded: 1
file:
- access_level: open_access
checksum: 7530a93ef42e10b4db1e5e4b69796e93
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T06:32:38Z
date_updated: 2023-07-18T06:32:38Z
file_id: '13243'
file_name: 2022_PMLR_vanderPlas.pdf
file_size: 585135
relation: main_file
success: 1
file_date_updated: 2023-07-18T06:32:38Z
has_accepted_license: '1'
intvolume: ' 199'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 518-531
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predictive learning enables neural networks to learn complex working memory
tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 199
year: '2022'
...
---
_id: '10753'
abstract:
- lang: eng
text: This is a comment on "Meta-learning synaptic plasticity and memory addressing
for continual familiarity detection." Neuron. 2022 Feb 2;110(3):544-557.e8.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Confavreux BJ, Vogels TP. A familiar thought: Machines that replace us? Neuron.
2022;110(3):361-362. doi:10.1016/j.neuron.2022.01.014'
apa: 'Confavreux, B. J., & Vogels, T. P. (2022). A familiar thought: Machines
that replace us? Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2022.01.014'
chicago: 'Confavreux, Basile J, and Tim P Vogels. “A Familiar Thought: Machines
That Replace Us?” Neuron. Elsevier, 2022. https://doi.org/10.1016/j.neuron.2022.01.014.'
ieee: 'B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace
us?,” Neuron, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.'
ista: 'Confavreux BJ, Vogels TP. 2022. A familiar thought: Machines that replace
us? Neuron. 110(3), 361–362.'
mla: 'Confavreux, Basile J., and Tim P. Vogels. “A Familiar Thought: Machines That
Replace Us?” Neuron, vol. 110, no. 3, Elsevier, 2022, pp. 361–62, doi:10.1016/j.neuron.2022.01.014.'
short: B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 361–362.
corr_author: '1'
date_created: 2022-02-13T23:01:34Z
date_published: 2022-02-02T00:00:00Z
date_updated: 2024-10-09T21:01:34Z
day: '02'
department:
- _id: TiVo
doi: 10.1016/j.neuron.2022.01.014
external_id:
isi:
- '000751819100005'
pmid:
- '35114107'
intvolume: ' 110'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.neuron.2022.01.014
month: '02'
oa: 1
oa_version: Published Version
page: 361-362
pmid: 1
publication: Neuron
publication_identifier:
eissn:
- 1097-4199
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'A familiar thought: Machines that replace us?'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2022'
...
---
_id: '11143'
abstract:
- lang: eng
text: 'Dravet syndrome is a neurodevelopmental disorder characterized by epilepsy,
intellectual disability, and sudden death due to pathogenic variants in SCN1A
with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing
parvalbumin GABAergic interneurons (PV-INs) from young Scn1a+/− mice show impaired
action potential generation. An approach assessing PV-IN function in the same
mice at two time points shows impaired spike generation in all Scn1a+/− mice at
postnatal days (P) 16–21, whether deceased prior or surviving to P35, with normalization
by P35 in surviving mice. However, PV-IN synaptic transmission is dysfunctional
in young Scn1a+/− mice that did not survive and in Scn1a+/− mice ≥ P35. Modeling
confirms that PV-IN axonal propagation is more sensitive to decreased sodium conductance
than spike generation. These results demonstrate dynamic dysfunction in Dravet
syndrome: combined abnormalities of PV-IN spike generation and propagation drives
early disease severity, while ongoing dysfunction of synaptic transmission contributes
to chronic pathology.'
acknowledgement: We would like to thank Bernardo Rudy, Joanna Mattis, and Laura Mcgarry
for comments on a previous version of the manuscript; Xiaohong Zhang for expert
technical support and mouse colony maintenance; Melody Cheng for assistance with
generation of the graphical abstract; and Jennifer Kearney for the gift of Scn1a+/−
mice. This work was supported by the National Institute of Neurological Disorders
and Stroke of the National Institutes of Health under F31NS111803 (to K.M.G.) and
K08NS097633 and R01NS110869 (to E.M.G.), the Dravet Syndrome Foundation (to A.S.),
an ERC Consolidator Grant (SYNAPSEEK) (to T.P.V.), and the NOMIS Foundation through
the NOMIS Fellowships program at IST Austria (to C.C.). The graphical abstract was
prepared using BioRender software (BioRender.com).
article_number: '110580'
article_processing_charge: No
article_type: original
author:
- first_name: Keisuke
full_name: Kaneko, Keisuke
last_name: Kaneko
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Kevin M.
full_name: Goff, Kevin M.
last_name: Goff
- first_name: Eric R.
full_name: Wengert, Eric R.
last_name: Wengert
- first_name: Ala
full_name: Somarowthu, Ala
last_name: Somarowthu
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Ethan M.
full_name: Goldberg, Ethan M.
last_name: Goldberg
citation:
ama: Kaneko K, Currin C, Goff KM, et al. Developmentally regulated impairment of
parvalbumin interneuron synaptic transmission in an experimental model of Dravet
syndrome. Cell Reports. 2022;38(13). doi:10.1016/j.celrep.2022.110580
apa: Kaneko, K., Currin, C., Goff, K. M., Wengert, E. R., Somarowthu, A., Vogels,
T. P., & Goldberg, E. M. (2022). Developmentally regulated impairment of parvalbumin
interneuron synaptic transmission in an experimental model of Dravet syndrome.
Cell Reports. Elsevier. https://doi.org/10.1016/j.celrep.2022.110580
chicago: Kaneko, Keisuke, Christopher Currin, Kevin M. Goff, Eric R. Wengert, Ala
Somarowthu, Tim P Vogels, and Ethan M. Goldberg. “Developmentally Regulated Impairment
of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of Dravet
Syndrome.” Cell Reports. Elsevier, 2022. https://doi.org/10.1016/j.celrep.2022.110580.
ieee: K. Kaneko et al., “Developmentally regulated impairment of parvalbumin
interneuron synaptic transmission in an experimental model of Dravet syndrome,”
Cell Reports, vol. 38, no. 13. Elsevier, 2022.
ista: Kaneko K, Currin C, Goff KM, Wengert ER, Somarowthu A, Vogels TP, Goldberg
EM. 2022. Developmentally regulated impairment of parvalbumin interneuron synaptic
transmission in an experimental model of Dravet syndrome. Cell Reports. 38(13),
110580.
mla: Kaneko, Keisuke, et al. “Developmentally Regulated Impairment of Parvalbumin
Interneuron Synaptic Transmission in an Experimental Model of Dravet Syndrome.”
Cell Reports, vol. 38, no. 13, 110580, Elsevier, 2022, doi:10.1016/j.celrep.2022.110580.
short: K. Kaneko, C. Currin, K.M. Goff, E.R. Wengert, A. Somarowthu, T.P. Vogels,
E.M. Goldberg, Cell Reports 38 (2022).
date_created: 2022-04-10T22:01:39Z
date_published: 2022-03-29T00:00:00Z
date_updated: 2025-06-11T14:00:11Z
day: '29'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1016/j.celrep.2022.110580
ec_funded: 1
external_id:
isi:
- '000779794000001'
pmid:
- '35354025'
file:
- access_level: open_access
checksum: 49105c6c27c9af0f37f50a8bbb4d380d
content_type: application/pdf
creator: dernst
date_created: 2022-04-15T11:00:58Z
date_updated: 2022-04-15T11:00:58Z
file_id: '11172'
file_name: 2022_CellReports_Kaneko.pdf
file_size: 4774216
relation: main_file
success: 1
file_date_updated: 2022-04-15T11:00:58Z
has_accepted_license: '1'
intvolume: ' 38'
isi: 1
issue: '13'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A
name: NOMIS Fellowship Program
publication: Cell Reports
publication_identifier:
eissn:
- 2211-1247
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Developmentally regulated impairment of parvalbumin interneuron synaptic transmission
in an experimental model of Dravet syndrome
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2022'
...
---
_id: '11453'
abstract:
- lang: eng
text: "Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological
properties. Synaptic connectivity determines which inputs from presynaptic neurons
are integrated, while cellular properties determine how inputs are filtered over
time. Unlike their biological counterparts, most computational approaches to learning
in simulated neural networks are limited to changes in synaptic connectivity.
However, if intrinsic parameters change, neural computations are altered drastically.
Here, we include the parameters that determine the intrinsic properties,\r\ne.g.,
time constants and reset potential, into the learning paradigm. Using sparse feedback
signals that indicate target spike times, and gradient-based parameter updates,
we show that the intrinsic parameters can be learned along with the synaptic weights
to produce specific input-output functions. Specifically, we use a teacher-student
paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire
neuron must recover the parameters of a teacher neuron. We show that complex temporal
functions can be learned online and without backpropagation through time, relying
on event-based updates only. Our results are a step towards online learning of
neural computations from ungraded and unsigned sparse feedback signals with a
biologically inspired learning mechanism."
acknowledgement: We would like to thank Professor Dr. Henning Sprekeler for his valuable
suggestions and Dr. Andrew Saxe, Milan Klöwer and Anna Wallis for their constructive
feedback on the manuscript. Lukas Braun was supported by the Network of European
Neuroscience Schools through their NENS Exchange Grant program, by the European
Union through their European Community Action Scheme for the Mobility of University
Students, the Woodward Scholarship awarded by Wadham College, Oxford and the Medical
Research Council [MR/N013468/1]. Tim P. Vogels was supported by a Wellcome Trust
Senior Research Fellowship [214316/Z/18/Z].
article_processing_charge: No
author:
- first_name: Lukas
full_name: Braun, Lukas
last_name: Braun
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Braun L, Vogels TP. Online learning of neural computations from sparse temporal
feedback. In: Advances in Neural Information Processing Systems - 35th Conference
on Neural Information Processing Systems. Vol 20. Neural Information Processing
Systems Foundation; 2021:16437-16450.'
apa: 'Braun, L., & Vogels, T. P. (2021). Online learning of neural computations
from sparse temporal feedback. In Advances in Neural Information Processing
Systems - 35th Conference on Neural Information Processing Systems (Vol. 20,
pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.'
chicago: Braun, Lukas, and Tim P Vogels. “Online Learning of Neural Computations
from Sparse Temporal Feedback.” In Advances in Neural Information Processing
Systems - 35th Conference on Neural Information Processing Systems, 20:16437–50.
Neural Information Processing Systems Foundation, 2021.
ieee: L. Braun and T. P. Vogels, “Online learning of neural computations from sparse
temporal feedback,” in Advances in Neural Information Processing Systems -
35th Conference on Neural Information Processing Systems, Virtual, Online,
2021, vol. 20, pp. 16437–16450.
ista: 'Braun L, Vogels TP. 2021. Online learning of neural computations from sparse
temporal feedback. Advances in Neural Information Processing Systems - 35th Conference
on Neural Information Processing Systems. NeurIPS: Neural Information Processing
Systems vol. 20, 16437–16450.'
mla: Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from
Sparse Temporal Feedback.” Advances in Neural Information Processing Systems
- 35th Conference on Neural Information Processing Systems, vol. 20, Neural
Information Processing Systems Foundation, 2021, pp. 16437–50.
short: L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems
- 35th Conference on Neural Information Processing Systems, Neural Information
Processing Systems Foundation, 2021, pp. 16437–16450.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
corr_author: '1'
date_created: 2022-06-19T22:01:59Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2025-04-14T09:44:14Z
day: '01'
department:
- _id: TiVo
intvolume: ' 20'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 16437-16450
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: "Whatâ\x80\x99s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks."
publication: Advances in Neural Information Processing Systems - 35th Conference on
Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Online learning of neural computations from sparse temporal feedback
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2021'
...
---
_id: '8253'
abstract:
- lang: eng
text: Brains process information in spiking neural networks. Their intricate connections
shape the diverse functions these networks perform. In comparison, the functional
capabilities of models of spiking networks are still rudimentary. This shortcoming
is mainly due to the lack of insight and practical algorithms to construct the
necessary connectivity. Any such algorithm typically attempts to build networks
by iteratively reducing the error compared to a desired output. But assigning
credit to hidden units in multi-layered spiking networks has remained challenging
due to the non-differentiable nonlinearity of spikes. To avoid this issue, one
can employ surrogate gradients to discover the required connectivity in spiking
network models. However, the choice of a surrogate is not unique, raising the
question of how its implementation influences the effectiveness of the method.
Here, we use numerical simulations to systematically study how essential design
parameters of surrogate gradients impact learning performance on a range of classification
problems. We show that surrogate gradient learning is robust to different shapes
of underlying surrogate derivatives, but the choice of the derivative’s scale
can substantially affect learning performance. When we combine surrogate gradients
with a suitable activity regularization technique, robust information processing
can be achieved in spiking networks even at the sparse activity limit. Our study
provides a systematic account of the remarkable robustness of surrogate gradient
learning and serves as a practical guide to model functional spiking neural networks.
acknowledgement: F.Z. was supported by the Wellcome Trust (110124/Z/15/Z) and the
Novartis Research Foundation. T.P.V. was supported by a Wellcome Trust Sir Henry
Dale Research fellowship (WT100000), a Wellcome Trust Senior Research Fellowship
(214316/Z/18/Z), and an ERC Consolidator Grant SYNAPSEEK.
article_processing_charge: No
article_type: original
author:
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
orcid: 0000-0003-1883-644X
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Zenke F, Vogels TP. The remarkable robustness of surrogate gradient learning
for instilling complex function in spiking neural networks. Neural Computation.
2021;33(4):899-925. doi:10.1162/neco_a_01367
apa: Zenke, F., & Vogels, T. P. (2021). The remarkable robustness of surrogate
gradient learning for instilling complex function in spiking neural networks.
Neural Computation. MIT Press. https://doi.org/10.1162/neco_a_01367
chicago: Zenke, Friedemann, and Tim P Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation. MIT Press, 2021. https://doi.org/10.1162/neco_a_01367.
ieee: F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks,” Neural
Computation, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.
ista: Zenke F, Vogels TP. 2021. The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks. Neural Computation.
33(4), 899–925.
mla: Zenke, Friedemann, and Tim P. Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation, vol. 33, no. 4, MIT Press, 2021, pp. 899–925, doi:10.1162/neco_a_01367.
short: F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.
corr_author: '1'
date_created: 2020-08-12T12:08:24Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2025-04-14T09:44:14Z
day: '01'
ddc:
- '000'
- '570'
department:
- _id: TiVo
doi: 10.1162/neco_a_01367
ec_funded: 1
external_id:
isi:
- '000663433900003'
pmid:
- '33513328'
file:
- access_level: open_access
checksum: eac5a51c24c8989ae7cf9ae32ec3bc95
content_type: application/pdf
creator: dernst
date_created: 2022-04-08T06:05:39Z
date_updated: 2022-04-08T06:05:39Z
file_id: '11131'
file_name: 2021_NeuralComputation_Zenke.pdf
file_size: 1611614
relation: main_file
success: 1
file_date_updated: 2022-04-08T06:05:39Z
has_accepted_license: '1'
intvolume: ' 33'
isi: 1
issue: '4'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 899-925
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: "Whatâ\x80\x99s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks."
publication: Neural Computation
publication_identifier:
eissn:
- 1530-888X
issn:
- 0899-7667
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: The remarkable robustness of surrogate gradient learning for instilling complex
function in spiking neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 33
year: '2021'
...
---
_id: '8757'
abstract:
- lang: eng
text: Traditional scientific conferences and seminar events have been hugely disrupted
by the COVID-19 pandemic, paving the way for virtual forms of scientific communication
to take hold and be put to the test.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Panagiotis
full_name: Bozelos, Panagiotis
id: 52e9c652-2982-11eb-81d4-b43d94c63700
last_name: Bozelos
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Bozelos P, Vogels TP. Talking science, online. Nature Reviews Neuroscience.
2021;22(1):1-2. doi:10.1038/s41583-020-00408-6
apa: Bozelos, P., & Vogels, T. P. (2021). Talking science, online. Nature
Reviews Neuroscience. Springer Nature. https://doi.org/10.1038/s41583-020-00408-6
chicago: Bozelos, Panagiotis, and Tim P Vogels. “Talking Science, Online.” Nature
Reviews Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41583-020-00408-6.
ieee: P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews
Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.
ista: Bozelos P, Vogels TP. 2021. Talking science, online. Nature Reviews Neuroscience.
22(1), 1–2.
mla: Bozelos, Panagiotis, and Tim P. Vogels. “Talking Science, Online.” Nature
Reviews Neuroscience, vol. 22, no. 1, Springer Nature, 2021, pp. 1–2, doi:10.1038/s41583-020-00408-6.
short: P. Bozelos, T.P. Vogels, Nature Reviews Neuroscience 22 (2021) 1–2.
date_created: 2020-11-15T23:01:18Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2025-07-10T12:01:24Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s41583-020-00408-6
external_id:
isi:
- '000588256300001'
pmid:
- '33173190'
file:
- access_level: open_access
checksum: 7985d7dff94c086e35b94a911d78d9ad
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T10:34:22Z
date_updated: 2021-02-04T10:34:22Z
file_id: '9088'
file_name: 2021_NatureNeuroScience_Bozelos.pdf
file_size: 683634
relation: main_file
success: 1
file_date_updated: 2021-02-04T10:34:22Z
has_accepted_license: '1'
intvolume: ' 22'
isi: 1
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 1-2
pmid: 1
publication: Nature Reviews Neuroscience
publication_identifier:
eissn:
- 1471-0048
issn:
- 1471-003X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Talking science, online
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 22
year: '2021'
...
---
_id: '9228'
abstract:
- lang: eng
text: Legacy conferences are costly and time consuming, and exclude scientists lacking
various resources or abilities. During the 2020 pandemic, we created an online
conference platform, Neuromatch Conferences (NMC), aimed at developing technological
and cultural changes to make conferences more democratic, scalable, and accessible.
We discuss the lessons we learned.
acknowledgement: We thank all of our volunteers from the NMC conferences (list of
names in the appendix). We also thank the NSF for support from 1734220 to B.W.,
and DARPA for support to T.A.
article_processing_charge: No
article_type: original
author:
- first_name: Titipat
full_name: Achakulvisut, Titipat
last_name: Achakulvisut
- first_name: Tulakan
full_name: Ruangrong, Tulakan
last_name: Ruangrong
- first_name: Patrick
full_name: Mineault, Patrick
last_name: Mineault
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Megan A.K.
full_name: Peters, Megan A.K.
last_name: Peters
- first_name: Panayiota
full_name: Poirazi, Panayiota
last_name: Poirazi
- first_name: Christopher
full_name: Rozell, Christopher
last_name: Rozell
- first_name: Brad
full_name: Wyble, Brad
last_name: Wyble
- first_name: Dan F.M.
full_name: Goodman, Dan F.M.
last_name: Goodman
- first_name: Konrad Paul
full_name: Kording, Konrad Paul
last_name: Kording
citation:
ama: 'Achakulvisut T, Ruangrong T, Mineault P, et al. Towards democratizing and
automating online conferences: Lessons from the Neuromatch Conferences. Trends
in Cognitive Sciences. 2021;25(4):265-268. doi:10.1016/j.tics.2021.01.007'
apa: 'Achakulvisut, T., Ruangrong, T., Mineault, P., Vogels, T. P., Peters, M. A.
K., Poirazi, P., … Kording, K. P. (2021). Towards democratizing and automating
online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive
Sciences. Elsevier. https://doi.org/10.1016/j.tics.2021.01.007'
chicago: 'Achakulvisut, Titipat, Tulakan Ruangrong, Patrick Mineault, Tim P Vogels,
Megan A.K. Peters, Panayiota Poirazi, Christopher Rozell, Brad Wyble, Dan F.M.
Goodman, and Konrad Paul Kording. “Towards Democratizing and Automating Online
Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive
Sciences. Elsevier, 2021. https://doi.org/10.1016/j.tics.2021.01.007.'
ieee: 'T. Achakulvisut et al., “Towards democratizing and automating online
conferences: Lessons from the Neuromatch Conferences,” Trends in Cognitive
Sciences, vol. 25, no. 4. Elsevier, pp. 265–268, 2021.'
ista: 'Achakulvisut T, Ruangrong T, Mineault P, Vogels TP, Peters MAK, Poirazi P,
Rozell C, Wyble B, Goodman DFM, Kording KP. 2021. Towards democratizing and automating
online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive
Sciences. 25(4), 265–268.'
mla: 'Achakulvisut, Titipat, et al. “Towards Democratizing and Automating Online
Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive
Sciences, vol. 25, no. 4, Elsevier, 2021, pp. 265–68, doi:10.1016/j.tics.2021.01.007.'
short: T. Achakulvisut, T. Ruangrong, P. Mineault, T.P. Vogels, M.A.K. Peters, P.
Poirazi, C. Rozell, B. Wyble, D.F.M. Goodman, K.P. Kording, Trends in Cognitive
Sciences 25 (2021) 265–268.
date_created: 2021-03-07T23:01:25Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2023-08-07T13:59:07Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1016/j.tics.2021.01.007
external_id:
isi:
- '000627418000001'
pmid:
- '33608214'
file:
- access_level: open_access
checksum: 87e39ea7bd266b976e8631b66979214d
content_type: application/pdf
creator: dernst
date_created: 2022-05-27T07:31:24Z
date_updated: 2022-05-27T07:31:24Z
file_id: '11415'
file_name: 2021_TrendsCognitiveSciences_Achakulvisut.pdf
file_size: 380720
relation: main_file
success: 1
file_date_updated: 2022-05-27T07:31:24Z
has_accepted_license: '1'
intvolume: ' 25'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
page: 265-268
pmid: 1
publication: Trends in Cognitive Sciences
publication_identifier:
eissn:
- 1879-307X
issn:
- 1364-6613
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Towards democratizing and automating online conferences: Lessons from the
Neuromatch Conferences'
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 25
year: '2021'
...
---
_id: '8126'
abstract:
- lang: eng
text: Cortical areas comprise multiple types of inhibitory interneurons with stereotypical
connectivity motifs, but their combined effect on postsynaptic dynamics has been
largely unexplored. Here, we analyse the response of a single postsynaptic model
neuron receiving tuned excitatory connections alongside inhibition from two plastic
populations. Depending on the inhibitory plasticity rule, synapses remain unspecific
(flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the
neuron’s receptive field, i.e., its response to presynaptic stimuli, depends on
the modulatory state of inhibition. When both inhibitory populations are active,
inhibition balances excitation, resulting in uncorrelated postsynaptic responses
regardless of the inhibitory tuning profiles. Modulating the activity of a given
inhibitory population produces strong correlations to either preferred or non-preferred
inputs, in line with recent experimental findings showing dramatic context-dependent
changes of neurons’ receptive fields. We thus confirm that a neuron’s receptive
field doesn’t follow directly from the weight profiles of its presynaptic afferents.
article_processing_charge: No
article_type: original
author:
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
orcid: 0000-0001-7184-7311
- first_name: Andrea I.
full_name: Luppi, Andrea I.
last_name: Luppi
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Agnes EJ, Luppi AI, Vogels TP. Complementary inhibitory weight profiles emerge
from plasticity and allow attentional switching of receptive fields. The Journal
of Neuroscience. 2020;40(50):9634-9649. doi:10.1523/JNEUROSCI.0276-20.2020
apa: Agnes, E. J., Luppi, A. I., & Vogels, T. P. (2020). Complementary inhibitory
weight profiles emerge from plasticity and allow attentional switching of receptive
fields. The Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.0276-20.2020
chicago: Agnes, Everton J., Andrea I. Luppi, and Tim P Vogels. “Complementary Inhibitory
Weight Profiles Emerge from Plasticity and Allow Attentional Switching of Receptive
Fields.” The Journal of Neuroscience. Society for Neuroscience, 2020. https://doi.org/10.1523/JNEUROSCI.0276-20.2020.
ieee: E. J. Agnes, A. I. Luppi, and T. P. Vogels, “Complementary inhibitory weight
profiles emerge from plasticity and allow attentional switching of receptive fields,”
The Journal of Neuroscience, vol. 40, no. 50. Society for Neuroscience,
pp. 9634–9649, 2020.
ista: Agnes EJ, Luppi AI, Vogels TP. 2020. Complementary inhibitory weight profiles
emerge from plasticity and allow attentional switching of receptive fields. The
Journal of Neuroscience. 40(50), 9634–9649.
mla: Agnes, Everton J., et al. “Complementary Inhibitory Weight Profiles Emerge
from Plasticity and Allow Attentional Switching of Receptive Fields.” The Journal
of Neuroscience, vol. 40, no. 50, Society for Neuroscience, 2020, pp. 9634–49,
doi:10.1523/JNEUROSCI.0276-20.2020.
short: E.J. Agnes, A.I. Luppi, T.P. Vogels, The Journal of Neuroscience 40 (2020)
9634–9649.
date_created: 2020-07-16T12:25:04Z
date_published: 2020-12-09T00:00:00Z
date_updated: 2023-08-22T07:54:26Z
day: '09'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1523/JNEUROSCI.0276-20.2020
external_id:
isi:
- '000606706400009'
pmid:
- '33168622'
file:
- access_level: open_access
checksum: 7977e4dd6b89357d1a5cc88babac56da
content_type: application/pdf
creator: dernst
date_created: 2020-12-28T08:31:47Z
date_updated: 2020-12-28T08:31:47Z
file_id: '8977'
file_name: 2020_JourNeuroscience_Agnes.pdf
file_size: 2750920
relation: main_file
success: 1
file_date_updated: 2020-12-28T08:31:47Z
has_accepted_license: '1'
intvolume: ' 40'
isi: 1
issue: '50'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 9634-9649
pmid: 1
publication: The Journal of Neuroscience
publication_identifier:
eissn:
- 1529-2401
publication_status: published
publisher: Society for Neuroscience
quality_controlled: '1'
scopus_import: '1'
status: public
title: Complementary inhibitory weight profiles emerge from plasticity and allow attentional
switching of receptive fields
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 40
year: '2020'
...
---
_id: '8127'
abstract:
- lang: eng
text: Mechanistic modeling in neuroscience aims to explain observed phenomena in
terms of underlying causes. However, determining which model parameters agree
with complex and stochastic neural data presents a significant challenge. We address
this challenge with a machine learning tool which uses deep neural density estimators—trained
using model simulations—to carry out Bayesian inference and retrieve the full
space of parameters compatible with raw data or selected data features. Our method
is scalable in parameters and data features and can rapidly analyze new data after
initial training. We demonstrate the power and flexibility of our approach on
receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize
the space of circuit configurations giving rise to rhythmic activity in the crustacean
stomatogastric ganglion, and use these results to derive hypotheses for underlying
compensation mechanisms. Our approach will help close the gap between data-driven
and theory-driven models of neural dynamics.
acknowledgement: We thank Mahmood S Hoseini and Michael Stryker for sharing their
data for Figure 2, and Philipp Berens, Sean Bittner, Jan Boelts, John Cunningham,
Richard Gao, Scott Linderman, Eve Marder, Iain Murray, George Papamakarios, Astrid
Prinz, Auguste Schulz and Srinivas Turaga for discussions and/or comments on the
manuscript. This work was supported by the German Research Foundation (DFG) through
SFB 1233 ‘Robust Vision’, (276693517), SFB 1089 ‘Synaptic Microcircuits’, SPP 2041
‘Computational Connectomics’ and Germany's Excellence Strategy – EXC-Number 2064/1
– Project number 390727645 and the German Federal Ministry of Education and Research
(BMBF, project ‘ADIMEM’, FKZ 01IS18052 A-D) to JHM, a Sir Henry Dale Fellowship
by the Wellcome Trust and the Royal Society (WT100000; WFP and TPV), a Wellcome
Trust Senior Research Fellowship (214316/Z/18/Z; TPV), a ERC Consolidator Grant
(SYNAPSEEK; WPF and CC), and a UK Research and Innovation, Biotechnology and Biological
Sciences Research Council (CC, UKRI-BBSRC BB/N019512/1). We gratefully acknowledge
the Leibniz Supercomputing Centre for funding this project by providing computing
time on its Linux-Cluster.
article_number: e56261
article_processing_charge: No
article_type: original
author:
- first_name: Pedro J.
full_name: Gonçalves, Pedro J.
last_name: Gonçalves
orcid: 0000-0002-6987-4836
- first_name: Jan-Matthis
full_name: Lueckmann, Jan-Matthis
last_name: Lueckmann
orcid: 0000-0003-4320-4663
- first_name: Michael
full_name: Deistler, Michael
last_name: Deistler
orcid: 0000-0002-3573-0404
- first_name: Marcel
full_name: Nonnenmacher, Marcel
last_name: Nonnenmacher
orcid: 0000-0001-6044-6627
- first_name: Kaan
full_name: Öcal, Kaan
last_name: Öcal
orcid: 0000-0002-8528-6858
- first_name: Giacomo
full_name: Bassetto, Giacomo
last_name: Bassetto
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: BA06AFEE-A4BA-11EA-AE5C-14673DDC885E
last_name: Chintaluri
orcid: 0000-0003-4252-1608
- first_name: William F.
full_name: Podlaski, William F.
last_name: Podlaski
orcid: 0000-0001-6619-7502
- first_name: Sara A.
full_name: Haddad, Sara A.
last_name: Haddad
orcid: 0000-0003-0807-0823
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: David S.
full_name: Greenberg, David S.
last_name: Greenberg
- first_name: Jakob H.
full_name: Macke, Jakob H.
last_name: Macke
orcid: 0000-0001-5154-8912
citation:
ama: Gonçalves PJ, Lueckmann J-M, Deistler M, et al. Training deep neural density
estimators to identify mechanistic models of neural dynamics. eLife. 2020;9.
doi:10.7554/eLife.56261
apa: Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K.,
Bassetto, G., … Macke, J. H. (2020). Training deep neural density estimators to
identify mechanistic models of neural dynamics. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.56261
chicago: Gonçalves, Pedro J., Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher,
Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, et al. “Training Deep Neural
Density Estimators to Identify Mechanistic Models of Neural Dynamics.” ELife.
eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.56261.
ieee: P. J. Gonçalves et al., “Training deep neural density estimators to
identify mechanistic models of neural dynamics,” eLife, vol. 9. eLife Sciences
Publications, 2020.
ista: Gonçalves PJ, Lueckmann J-M, Deistler M, Nonnenmacher M, Öcal K, Bassetto
G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. 2020.
Training deep neural density estimators to identify mechanistic models of neural
dynamics. eLife. 9, e56261.
mla: Gonçalves, Pedro J., et al. “Training Deep Neural Density Estimators to Identify
Mechanistic Models of Neural Dynamics.” ELife, vol. 9, e56261, eLife Sciences
Publications, 2020, doi:10.7554/eLife.56261.
short: P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G.
Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg,
J.H. Macke, ELife 9 (2020).
date_created: 2020-07-16T12:26:04Z
date_published: 2020-09-17T00:00:00Z
date_updated: 2025-04-14T07:54:31Z
day: '17'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.7554/eLife.56261
ec_funded: 1
external_id:
isi:
- '000584989400001'
pmid:
- '32940606'
file:
- access_level: open_access
checksum: c4300ddcd93ed03fc9c6cdf1f77890be
content_type: application/pdf
creator: cziletti
date_created: 2020-10-27T11:37:32Z
date_updated: 2020-10-27T11:37:32Z
file_id: '8709'
file_name: 2020_eLife_Gonçalves.pdf
file_size: 17355867
relation: main_file
success: 1
file_date_updated: 2020-10-27T11:37:32Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Training deep neural density estimators to identify mechanistic models of neural
dynamics
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
year: '2020'
...
---
_id: '9633'
abstract:
- lang: eng
text: The search for biologically faithful synaptic plasticity rules has resulted
in a large body of models. They are usually inspired by – and fitted to – experimental
data, but they rarely produce neural dynamics that serve complex functions. These
failures suggest that current plasticity models are still under-constrained by
existing data. Here, we present an alternative approach that uses meta-learning
to discover plausible synaptic plasticity rules. Instead of experimental data,
the rules are constrained by the functions they implement and the structure they
are meant to produce. Briefly, we parameterize synaptic plasticity rules by a
Volterra expansion and then use supervised learning methods (gradient descent
or evolutionary strategies) to minimize a problem-dependent loss function that
quantifies how effectively a candidate plasticity rule transforms an initially
random network into one with the desired function. We first validate our approach
by re-discovering previously described plasticity rules, starting at the single-neuron
level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction
of most variability of inputs to a neuron (i.e., the first principal component).
We expand the problem to the network level and ask the framework to find Oja’s
rule together with an anti-Hebbian rule such that an initially random two-layer
firing-rate network will recover several principal components of the input space
after learning. Next, we move to networks of integrate-and-fire neurons with plastic
inhibitory afferents. We train for rules that achieve a target firing rate by
countering tuned excitation. Our algorithm discovers a specific subset of the
manifold of rules that can solve this task. Our work is a proof of principle of
an automated and unbiased approach to unveil synaptic plasticity rules that obey
biological constraints and can solve complex functions.
acknowledgement: We would like to thank Chaitanya Chintaluri, Georgia Christodoulou,
Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work
was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z),
a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme
Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.
article_processing_charge: No
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Timothy
full_name: Lillicrap, Timothy
last_name: Lillicrap
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. In: Advances in Neural Information Processing Systems.
Vol 33. ; 2020:16398-16408.'
apa: Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., & Vogels, T.
P. (2020). A meta-learning approach to (re)discover plasticity rules that carve
a desired function into a neural network. In Advances in Neural Information
Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada.
chicago: Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap,
and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That
Carve a Desired Function into a Neural Network.” In Advances in Neural Information
Processing Systems, 33:16398–408, 2020.
ieee: B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A
meta-learning approach to (re)discover plasticity rules that carve a desired function
into a neural network,” in Advances in Neural Information Processing Systems,
Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.
ista: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference
on Neural Information Processing Systems vol. 33, 16398–16408.'
mla: Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity
Rules That Carve a Desired Function into a Neural Network.” Advances in Neural
Information Processing Systems, vol. 33, 2020, pp. 16398–408.
short: B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances
in Neural Information Processing Systems, 2020, pp. 16398–16408.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2021-07-04T22:01:27Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2026-04-29T22:30:21Z
day: '06'
department:
- _id: TiVo
ec_funded: 1
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 16398-16408
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Advances in Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: is_continued_by
url: https://doi.org/10.1101/2020.10.24.353409
record:
- id: '14422'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A meta-learning approach to (re)discover plasticity rules that carve a desired
function into a neural network
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 33
year: '2020'
...
---
_id: '8013'
article_number: e1007049
article_processing_charge: No
article_type: original
author:
- first_name: Christopher B.
full_name: Currin, Christopher B.
last_name: Currin
- first_name: Phumlani N.
full_name: Khoza, Phumlani N.
last_name: Khoza
- first_name: Alexander D.
full_name: Antrobus, Alexander D.
last_name: Antrobus
- first_name: Peter E.
full_name: Latham, Peter E.
last_name: Latham
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Joseph V.
full_name: Raimondo, Joseph V.
last_name: Raimondo
citation:
ama: 'Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. Think:
Theory for Africa. PLOS Computational Biology. 2019;15(7). doi:10.1371/journal.pcbi.1007049'
apa: 'Currin, C. B., Khoza, P. N., Antrobus, A. D., Latham, P. E., Vogels, T. P.,
& Raimondo, J. V. (2019). Think: Theory for Africa. PLOS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007049'
chicago: 'Currin, Christopher B., Phumlani N. Khoza, Alexander D. Antrobus, Peter
E. Latham, Tim P Vogels, and Joseph V. Raimondo. “Think: Theory for Africa.” PLOS
Computational Biology. Public Library of Science, 2019. https://doi.org/10.1371/journal.pcbi.1007049.'
ieee: 'C. B. Currin, P. N. Khoza, A. D. Antrobus, P. E. Latham, T. P. Vogels, and
J. V. Raimondo, “Think: Theory for Africa,” PLOS Computational Biology,
vol. 15, no. 7. Public Library of Science, 2019.'
ista: 'Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. 2019.
Think: Theory for Africa. PLOS Computational Biology. 15(7), e1007049.'
mla: 'Currin, Christopher B., et al. “Think: Theory for Africa.” PLOS Computational
Biology, vol. 15, no. 7, e1007049, Public Library of Science, 2019, doi:10.1371/journal.pcbi.1007049.'
short: C.B. Currin, P.N. Khoza, A.D. Antrobus, P.E. Latham, T.P. Vogels, J.V. Raimondo,
PLOS Computational Biology 15 (2019).
date_created: 2020-06-25T12:50:39Z
date_published: 2019-07-11T00:00:00Z
date_updated: 2021-01-12T08:16:31Z
day: '11'
ddc:
- '570'
doi: 10.1371/journal.pcbi.1007049
extern: '1'
external_id:
pmid:
- '31295253'
file:
- access_level: open_access
checksum: 723bdfb6ee5c747cbbb32baf01d17fad
content_type: application/pdf
creator: cziletti
date_created: 2020-07-02T12:22:57Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8079'
file_name: 2019_PlosCompBio_Currin.pdf
file_size: 773969
relation: main_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
intvolume: ' 15'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
status: public
title: 'Think: Theory for Africa'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 15
year: '2019'
...