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

Leenoy Meshulam; Jeffrey L Gauthier; Carlos D Brody; David W Tank; William Bialek

doi:10.1016/j.neuron.2017.10.027

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

Neuron. 2017 Dec 6;96(5):1178-1191.e4. doi: 10.1016/j.neuron.2017.10.027. Epub 2017 Nov 16.

Authors

Leenoy Meshulam¹, Jeffrey L Gauthier², Carlos D Brody³, David W Tank⁴, William Bialek⁵

Affiliations

¹ Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Electronic address: leenoy@princeton.edu.
² Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
³ Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA; Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544, USA.
⁴ Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
⁵ Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

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. 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.

Keywords: collective phenomena; hippocampus; maximum entropy; pairwise correlations; place cells.

MeSH terms

Action Potentials
Algorithms
Animals
CA1 Region, Hippocampal / cytology
CA1 Region, Hippocampal / physiology
Entropy
Hippocampus / cytology*
Hippocampus / physiology*
Male
Mice
Mice, Inbred C57BL
Mice, Transgenic
Models, Neurological
Nerve Net / cytology*
Nerve Net / physiology*
Neurons / physiology*
Photic Stimulation
Space Perception / physiology*
User-Computer Interface

Grants and funding

HHMI/Howard Hughes Medical Institute/United States