Collective behavior of place and non-place neurons in the hippocampal network

TL;DR

This study investigates the collective activity of place and non-place neurons in the hippocampus, revealing that they form an integrated network encoding more than just spatial information, using optical imaging and maximum entropy modeling.

Contribution

It demonstrates that place and non-place cells are part of a unified network, with simple models accurately predicting neuronal activity based on collective states.

Findings

Place and non-place neurons form an integrated network.

Maximum entropy models predict neuron activity from network state.

Place cells are not a distinct sub-network.

Abstract

Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track, and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. These and other results suggest that place cells are not a distinct sub-network, but part of a larger system that encodes, collectively, more than just place information.