---
OA_place: publisher
OA_type: hybrid
_id: '17886'
abstract:
- lang: eng
text: 'Thin pancake-like neuronal networks cultured on top of a planar microelectrode
array have been extensively tried out in neuroengineering, as a substrate for
the mobile robot’s control unit, i.e., as a cyborg’s brain. Most of these attempts
failed due to intricate self-organizing dynamics in the neuronal systems. In particular,
the networks may exhibit an emergent spatial map of steady nucleation sites (“n-sites”)
of spontaneous population spikes. Being unpredictable and independent of the surface
electrode locations, the n-sites drastically change local ability of the network
to generate spikes. Here, using a spiking neuronal network model with generative
spatially-embedded connectome, we systematically show in simulations that the
number, location, and relative activity of spontaneously formed n-sites (“the
vitals”) crucially depend on the samplings of three distributions: (1) the network
distribution of neuronal excitability, (2) the distribution of connections between
neurons of the network, and (3) the distribution of maximal amplitudes of a single
synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about
4%) of non-pacemaker neurons having the highest excitability was enough to completely
suppress the occurrence of population spikes and their n-sites. This key result
is explained theoretically. Remarkably, the n-sites occur taking into account
only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity.
As the spiking network model used in this study is strictly deterministic, all
simulation results can be accurately reproduced. The model, which has already
demonstrated a very high richness-to-complexity ratio, can also be directly extended
into the three-dimensional case, e.g., for targeting peculiarities of spiking
dynamics in cerebral (or brain) organoids. We recommend the model as an excellent
illustrative tool for teaching network-level computational neuroscience, complementing
a few benchmark models.'
acknowledgement: A.P. is grateful to Chaitanya Chintaluri, Douglas Feitosa Tomé, and
Tim P. Vogels for useful discussions. This work was supported by a European Research
Council Consolidator Grant (SYNAPSEEK, 819603, to Tim P. Vogels).
article_number: '106589'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Dmitrii
full_name: Zendrikov, Dmitrii
last_name: Zendrikov
- first_name: Alexander
full_name: Paraskevov, Alexander
id: d05e3c56-9262-11ed-9231-be692464e5ac
last_name: Paraskevov
citation:
ama: Zendrikov D, Paraskevov A. The vitals for steady nucleation maps of spontaneous
spiking coherence in autonomous two-dimensional neuronal networks. Neural Networks.
2024;180. doi:10.1016/j.neunet.2024.106589
apa: Zendrikov, D., & Paraskevov, A. (2024). The vitals for steady nucleation
maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks.
Neural Networks. Elsevier. https://doi.org/10.1016/j.neunet.2024.106589
chicago: Zendrikov, Dmitrii, and Alexander Paraskevov. “The Vitals for Steady Nucleation
Maps of Spontaneous Spiking Coherence in Autonomous Two-Dimensional Neuronal Networks.”
Neural Networks. Elsevier, 2024. https://doi.org/10.1016/j.neunet.2024.106589.
ieee: D. Zendrikov and A. Paraskevov, “The vitals for steady nucleation maps of
spontaneous spiking coherence in autonomous two-dimensional neuronal networks,”
Neural Networks, vol. 180. Elsevier, 2024.
ista: Zendrikov D, Paraskevov A. 2024. The vitals for steady nucleation maps of
spontaneous spiking coherence in autonomous two-dimensional neuronal networks.
Neural Networks. 180, 106589.
mla: Zendrikov, Dmitrii, and Alexander Paraskevov. “The Vitals for Steady Nucleation
Maps of Spontaneous Spiking Coherence in Autonomous Two-Dimensional Neuronal Networks.”
Neural Networks, vol. 180, 106589, Elsevier, 2024, doi:10.1016/j.neunet.2024.106589.
short: D. Zendrikov, A. Paraskevov, Neural Networks 180 (2024).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: The attached Supplementary Material for this article contains
four Figures, S1–S4, and two Videos. The simulation data and program codes required
for reproducing all results of this study are available online at https://doi.org/10.6084/m9.figshare.24527848
date_created: 2024-09-08T22:01:10Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2026-10-21T11:57:08Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1016/j.neunet.2024.106589
ec_funded: 1
external_id:
isi:
- '001316474600001'
pmid:
- '39217864'
file:
- access_level: open_access
checksum: 6a194323234e01d4ae725f674529cdb1
content_type: application/pdf
creator: dernst
date_created: 2025-01-13T08:26:08Z
date_updated: 2025-01-13T08:26:08Z
file_id: '18825'
file_name: 2024_NeuralNetworks_Zendrikov.pdf
file_size: 6162281
relation: main_file
success: 1
file_date_updated: 2025-01-13T08:26:08Z
fulldoi: https://doi.org/10.1016/j.neunet.2024.106589
has_accepted_license: '1'
intvolume: ' 180'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '12'
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: Neural Networks
publication_identifier:
eissn:
- 1879-2782
issn:
- 0893-6080
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
record:
- id: '23182'
relation: research_data
status: public
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous
two-dimensional neuronal 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: 180
year: '2024'
...