[{"ec_funded":1,"OA_place":"publisher","das_tickbox":"1","author":[{"last_name":"Zendrikov","full_name":"Zendrikov, Dmitrii","first_name":"Dmitrii"},{"first_name":"Alexander","full_name":"Paraskevov, Alexander","last_name":"Paraskevov","id":"d05e3c56-9262-11ed-9231-be692464e5ac"}],"isi":1,"day":"01","article_type":"original","OA_type":"hybrid","pmid":1,"quality_controlled":"1","file":[{"creator":"dernst","file_size":6162281,"success":1,"file_id":"18825","content_type":"application/pdf","checksum":"6a194323234e01d4ae725f674529cdb1","date_updated":"2025-01-13T08:26:08Z","relation":"main_file","file_name":"2024_NeuralNetworks_Zendrikov.pdf","access_level":"open_access","date_created":"2025-01-13T08:26:08Z"}],"volume":180,"supplementarymaterial":"yes","publication_status":"published","fulldoi":"https://doi.org/10.1016/j.neunet.2024.106589","publication":"Neural Networks","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"intvolume":"       180","related_material":{"record":[{"status":"public","id":"23182","relation":"research_data"}]},"status":"public","year":"2024","researchdata_availability":"yes","type":"journal_article","date_created":"2024-09-08T22:01:10Z","oa":1,"date_published":"2024-12-01T00:00:00Z","oa_version":"Published Version","language":[{"iso":"eng"}],"month":"12","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","scopus_import":"1","project":[{"grant_number":"819603","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"article_number":"106589","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","article_processing_charge":"Yes (via OA deal)","doi":"10.1016/j.neunet.2024.106589","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>.","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.","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.","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>"},"file_date_updated":"2025-01-13T08:26:08Z","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."}],"publication_identifier":{"issn":["0893-6080"],"eissn":["1879-2782"]},"corr_author":"1","has_accepted_license":"1","department":[{"_id":"TiVo"}],"_id":"17886","publisher":"Elsevier","ddc":["570"],"date_updated":"2026-10-21T11:57:08Z","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).","external_id":{"pmid":["39217864"],"isi":["001316474600001"]},"title":"The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks"}]