Collective Behavior of Place and Non-place Neurons in the Hippocampal Network

• Published in: Neuron (Cambridge, Mass.) Vol. 96; no. 5; pp. 1178 - 1191.e4
• Main Authors: Meshulam, Leenoy, Gauthier, Jeffrey L., Brody, Carlos D., Tank, David W., Bialek, William
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 06.12.2017; Elsevier Limited
• Subjects: Action Potentials; Algorithms; Animal models; Animals; CA1 Region, Hippocampal - cytology; CA1 Region, Hippocampal - physiology; Cognitive models; collective phenomena; Entropy; Hippocampus; Hippocampus - cytology; Hippocampus - physiology; Male; maximum entropy; Memory; Mice; Mice, Inbred C57BL; Mice, Transgenic; Models, Neurological; Nerve Net - cytology; Nerve Net - physiology; Neurons; Neurons - physiology; pairwise correlations; Phenomenology; Photic Stimulation; place cells; Population; Rodents; Space Perception - physiology; Studies; User-Computer Interface; Virtual reality; maximum entropy; collective phenomena; hippocampus; pairwise correlations; place cells
• ISSN: 0896-6273; 1097-4199
• DOI: 10.1016/j.neuron.2017.10.027

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. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state. •A successful unified theoretical framework for population states•Maximum entropy model predictions have high precision agreement with data•Network interactions explain a substantial amount of population activity in CA1•Place cells and non-place cells encode information collectively Correlation patterns in CA1 hippocampus only partially arise from place encoding. Meshulam et al. utilize a population-level modeling approach to uncover collective patterns of activity in CA1 neurons that substantially reflect not only position but also their internal network state.