Higher-order interactions in neural populations

NATALI GUISANDE; MARIELA PORTESI; FERNANDO MONTANI

Congreso; XI Argentinian Congress of Bioinformatics and Computational Biology; 2021

Higher-order interactions in neural populationsBackground:Advances in multi-unit recordings make possible the investigation of the simultaneous activity of a very large number of neurons. Previous research has proved that the activity of all neuronal inputs follows a nonGaussian distribution that shapes the population response. We have recently developed a dichotomized q-Gaussian population model of easy numerical implementation, that can capture the subtle changes of the input?s heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons, and higher-order correlations in the spiking output activity are quantified by the parameter q.Results:This work presents an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, by using a combination of tools of graph theory and information theory. It shows under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.Conclusions:The results allow us to characterize the population firing activity obtained from simultaneous recordings of neurons across all layers of a simulated cortical microcolumn. We put special emphasis on how to perform computational estimations efficiently when considering higher-order interactions in the input connectivity. This enables us to precisely quantify the amount of correlations that are generated from higher-order interconnectivity of the common overlapping neuronal inputs.