Abstract
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I study the problem of storing associations between analog signals in the presence of correlations, using methods from statistical mechanics. I characterize the typical learning performance in terms of the power spectrum of random input and output processes. I show that optimal synaptic weight configurations reach a capacity of 0.5 for any fraction of excitatory to inhibitory weights and have a peculiar synaptic distribution with a finite fraction of silent synapses. I further provide a link between typical learning performance and principal components analysis in single cases. These results may shed light on the synaptic profile of brain circuits, such as cerebellar structures, that are thought to engage in processing time-dependent signals and performing on-line prediction.
Funder
Gatsby Charitable Foundation
NSF NeuroNex Award
Publisher
Public Library of Science (PLoS)
Subject
Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics
Reference67 articles.
1. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework;HF Song;PLOS Computational Biology,2016
2. Supervised learning in spiking neural networks with FORCE training;W Nicola;Nature Communications,2017
3. Training dynamically balanced excitatory-inhibitory networks;A Ingrosso;PLOS ONE,2019
4. Learning recurrent dynamics in spiking networks;CM Kim;eLife,2018
5. Learning to represent signals spike by spike;W Brendel;PLOS Computational Biology,2020
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