---
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. <i>Neural Networks</i>.
    2024;180. doi:<a href="https://doi.org/10.1016/j.neunet.2024.106589">10.1016/j.neunet.2024.106589</a>
  apa: Zendrikov, D., &#38; Paraskevov, A. (2024). The vitals for steady nucleation
    maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks.
    <i>Neural Networks</i>. Elsevier. <a href="https://doi.org/10.1016/j.neunet.2024.106589">https://doi.org/10.1016/j.neunet.2024.106589</a>
  chicago: Zendrikov, Dmitrii, and Alexander Paraskevov. “The Vitals for Steady Nucleation
    Maps of Spontaneous Spiking Coherence in Autonomous Two-Dimensional Neuronal Networks.”
    <i>Neural Networks</i>. Elsevier, 2024. <a href="https://doi.org/10.1016/j.neunet.2024.106589">https://doi.org/10.1016/j.neunet.2024.106589</a>.
  ieee: D. Zendrikov and A. Paraskevov, “The vitals for steady nucleation maps of
    spontaneous spiking coherence in autonomous two-dimensional neuronal networks,”
    <i>Neural Networks</i>, 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.”
    <i>Neural Networks</i>, vol. 180, 106589, Elsevier, 2024, doi:<a href="https://doi.org/10.1016/j.neunet.2024.106589">10.1016/j.neunet.2024.106589</a>.
  short: D. Zendrikov, A. Paraskevov, Neural Networks 180 (2024).
corr_author: '1'
date_created: 2024-09-08T22:01:10Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-09-08T09:12:20Z
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
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'
scopus_import: '1'
status: public
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'
...