{"publication_identifier":{"issn":["0893-6080"],"eissn":["1879-2782"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"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.","lang":"eng"}],"citation":{"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>.","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.","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>","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.","short":"D. Zendrikov, A. Paraskevov, Neural Networks 180 (2024).","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>.","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>"},"title":"The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks","corr_author":"1","date_updated":"2024-09-09T08:52:43Z","project":[{"grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020"}],"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).","type":"journal_article","article_number":"106589","status":"public","pmid":1,"_id":"17886","year":"2024","date_published":"2024-08-31T00:00:00Z","oa":1,"article_type":"original","publisher":"Elsevier","publication_status":"epub_ahead","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)"},"external_id":{"pmid":["39217864"]},"department":[{"_id":"TiVo"}],"author":[{"full_name":"Zendrikov, Dmitrii","first_name":"Dmitrii","last_name":"Zendrikov"},{"full_name":"Paraskevov, Alexander","id":"d05e3c56-9262-11ed-9231-be692464e5ac","last_name":"Paraskevov","first_name":"Alexander"}],"article_processing_charge":"Yes (in subscription journal)","doi":"10.1016/j.neunet.2024.106589","ec_funded":1,"language":[{"iso":"eng"}],"day":"31","quality_controlled":"1","date_created":"2024-09-08T22:01:10Z","has_accepted_license":"1","oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.1016/j.neunet.2024.106589","open_access":"1"}],"publication":"Neural Networks","license":"https://creativecommons.org/licenses/by/4.0/","month":"08","scopus_import":"1","volume":180,"intvolume":"       180","ddc":["570"]}