Abstract
Cortical circuits are characterized by a high degree of structural and
dynamical complexity, and this biological reality is reflected in the
large number of parameters in even highly idealized cortical models. A
fundamental task of computational neuroscience is to understand how
these parameters govern network dynamics. While some neuronal
parameters can be measured in vivo , many remain poorly constrained
due to limitations of available experimental techniques. Computational
models can address this problem by relating difficult-to-measure
parameters to observable quantities, but to do so one must overcome two
challenges: (1) the computational expense of mapping a high dimensional
parameter space, and (2) extracting biological insights from such a map.
This study aims to address these challenges in the following ways:
First, we propose a data-informed, parsimonious mean-field algorithm
that efficiently predicts spontaneous cortical activity, thereby
speeding up the mapping of parameter landscapes. Second, we show that
lateral inhibition provides a basis for conceptualizing cortical
parameter space, enabling us to begin to make sense of its geometric
structure and attendant scaling relations. We illustrate our approach
on a biologically realistic model of the Macaque primary visual cortex.