{"article_processing_charge":"Yes (via OA deal)","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).","isi":1,"author":[{"full_name":"Zendrikov, Dmitrii","first_name":"Dmitrii","last_name":"Zendrikov"},{"id":"d05e3c56-9262-11ed-9231-be692464e5ac","last_name":"Paraskevov","full_name":"Paraskevov, Alexander","first_name":"Alexander"}],"date_updated":"2025-09-08T09:12:20Z","year":"2024","type":"journal_article","scopus_import":"1","department":[{"_id":"TiVo"}],"volume":180,"intvolume":" 180","publisher":"Elsevier","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"}],"quality_controlled":"1","ddc":["570"],"has_accepted_license":"1","corr_author":"1","publication":"Neural Networks","date_published":"2024-12-01T00:00:00Z","OA_place":"publisher","citation":{"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","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.","short":"D. Zendrikov, A. Paraskevov, Neural Networks 180 (2024).","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.","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.","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","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."},"ec_funded":1,"file_date_updated":"2025-01-13T08:26:08Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","day":"01","pmid":1,"OA_type":"hybrid","publication_status":"published","oa_version":"Published Version","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"_id":"17886","fulldoi":"https://doi.org/10.1016/j.neunet.2024.106589","article_number":"106589","title":"The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks","doi":"10.1016/j.neunet.2024.106589","article_type":"original","language":[{"iso":"eng"}],"oa":1,"external_id":{"pmid":["39217864"],"isi":["001316474600001"]},"project":[{"grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"month":"12","file":[{"file_size":6162281,"success":1,"creator":"dernst","file_id":"18825","checksum":"6a194323234e01d4ae725f674529cdb1","relation":"main_file","date_updated":"2025-01-13T08:26:08Z","content_type":"application/pdf","date_created":"2025-01-13T08:26:08Z","file_name":"2024_NeuralNetworks_Zendrikov.pdf","access_level":"open_access"}],"status":"public","publication_identifier":{"eissn":["1879-2782"],"issn":["0893-6080"]},"date_created":"2024-09-08T22:01:10Z"}