The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks Zendrikov, Dmitrii Paraskevov, Alexander ddc:570 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. Elsevier 2024 info:eu-repo/semantics/article doc-type:Article Article http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/17886 https://research-explorer.ista.ac.at/download/17886/18825 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> eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neunet.2024.106589 info:eu-repo/semantics/altIdentifier/issn/0893-6080 info:eu-repo/semantics/altIdentifier/e-issn/1879-2782 info:eu-repo/semantics/altIdentifier/wos/001316474600001 info:eu-repo/semantics/altIdentifier/pmid/39217864 info:eu-repo/semantics/openAccess