Correlated connectivity and the distribution of firing rates in the neocortex

TL;DR

This paper presents a simple cortical model that explains the lognormal distribution of firing rates and synaptic strengths, predicting correlated connectivity patterns that reconcile sparse activity with sparse connectivity in the neocortex.

Contribution

It introduces a Hebb-like learning rule that accounts for both lognormal firing rate and synaptic efficacy distributions, linking activity and connectivity.

Findings

Lognormal distribution of firing rates and synaptic strengths.

Predicted correlation in synaptic efficacies onto individual neurons.

Reconciliation of sparse activity with sparse connectivity in cortical networks.

Abstract

Two recent experimental observations pose a challenge to many cortical models. First, the activity in the auditory cortex is sparse, and firing rates can be described by a lognormal distribution. Second, the distribution of non-zero synaptic strengths between nearby cortical neurons can also be described by a lognormal distribution. Here we use a simple model of cortical activity to reconcile these observations. The model makes the experimentally testable prediction that synaptic efficacies onto a given cortical neuron are statistically correlated, i.e. it predicts that some neurons receive many more strong connections than other neurons. We propose a simple Hebb-like learning rule which gives rise to both lognormal firing rates and synaptic efficacies. Our results represent a first step toward reconciling sparse activity and sparse connectivity in cortical networks.