Robust Cortical Criticality and Diverse Neural Network Dynamics Resulting from Functional Specification

Abstract

Despite recognized layered structure and increasing evidence for criticality in the cortex, how the specification of input, output and computational layers affects the self-organized criticality has been surprisingly neglected. By constructing heterogeneous structures with a well-accepted model of leaky neurons, we found that the specification can lead to robust criticality almost insensitive to the strength of external stimuli. This naturally unifies the adaptation to strong inputs without extra synaptic plasticity mechanisms. Presence of output neurons constitutes an alternative explanation to subcriticality other than the high frequency inputs. Degree of recurrence is proposed as a network metric to account for the signal termination due to output neurons. Unlike fully recurrent networks where external stimuli always render subcriticality, the dynamics of networks with sufficient feed-forward connections can be driven to criticality and supercriticality. These findings indicate that functional and structural specification and their interplay with external stimuli are of crucial importance for the network dynamics. The robust criticality puts forward networks of the leaky neurons as a promising platform for realizing artificial neural networks that work in the vicinity of critical points.