Control of recurrent neural network dynamics by homeostatic intrinsic plasticity - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Control of recurrent neural network dynamics by homeostatic intrinsic plasticity

Abstract

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.
GIF.odp (1.1 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01225622 , version 1 (10-12-2015)

Identifiers

  • HAL Id : hal-01225622 , version 1

Cite

Bruno Cessac, Jérémie Naudé, Hugues Berry, Bruno Delord. Control of recurrent neural network dynamics by homeostatic intrinsic plasticity. Workshop on neural population dynamics, Feb 2015, Gif sur Yvette, France. ⟨hal-01225622⟩
379 View
8 Download

Share

Gmail Facebook X LinkedIn More