Control of recurrent neural network dynamics by homeostatic intrinsic plasticity
Résumé
Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal
activity, cardinal for the proper functioning of nervous systems. Here,
we assess how cellular HIP effects translate into collective dynamics and computational
properties in biological recurrent networks. We develop a realistic multi-scale model
including a generic HIP rule regulating the neuronal threshold with actual molecular signaling
pathways kinetics, Dale’s principle, sparse connectivity, synaptic balance and Hebbian
synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a
working point at which inputs are transduced by large derivative ranges of the transfer
function. This cellular mechanism insures increased network dynamics complexity, robust
balance with SP at the edge of chaos, and improved input separability. Although critically
dependent upon balanced excitatory and inhibitory drives, these effects display striking
robustness to changes in network architecture, learning rates and input features. Thus, the
mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in
neural networks. Understanding this robustness is an important challenge to unravel principles
underlying self-organization around criticality in biological recurrent neural networks.
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