Higher-order correlations in common input shapes the output spiking activity of a neural population

FERNANDO MONTANI; LISANDRO MONTANGIE; FERNANDO MONTANI; LISANDRO MONTANGIE

PHYSICA A - STATISTICAL AND THEORETICAL PHYSICS

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2017 vol. 471 p. 845 - 861

0378-4371

Recent neurophysiological experiments suggest that populations of neurons use acomputational scheme in which spike timing is regulated by common non-Gaussianinputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons.We provide a novel formalism, of easy numerical implementation, that can capturethe subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.