An Analytical Framework for Multi-Area Balanced Networks

Josue Casco-Rodriguez, Mitra Javadzadeh

Keywords: excitation–inhibition balance, recurrent networks, cortical dynamics, multi- area networks, asymmetry, amplification

Abstract:

A hallmark of neocortical architecture is recurrent connectivity both within and between
local sub-networks (cortical areas). Within a cortical area, excitation-inhibition balance
(balanced positive and negative connections) shapes neural activity dynamics,
while reciprocal connections between areas are excitatory. How this multi-area structure
shapes neural dynamics remains largely unknown. We present an analytical
framework for balanced multi-area networks, revealing key features of cortical computation;
we find that local connectivity within an area determines its responses to inputs received
locally (extra-cortical), but not to inputs relayed from other cortical areas. Local responses
to these relayed inputs are instead primarily driven by long-range inter-area connections.
Moreover, we find that the asymmetry of inter-area connections (feedforward vs feedback
strength) can modulate the joint dynamics across areas and implement a tradeoff between
regimes that promote similarity or divergence of activity across areas.

Submission Number: 27