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Ma M, Simoes de Souza F, Futia GL, Anderson SR, Riguero J, Tollin D, Gentile-Polese A, Platt JP, Steinke K, Hiratani N, Gibson EA, Restrepo D. Sequential activity of CA1 hippocampal cells constitutes a temporal memory map for associative learning in mice. Curr Biol 2024; 34:841-854.e4. [PMID: 38325376 DOI: 10.1016/j.cub.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
Sequential neural dynamics encoded by time cells play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. We used two-photon Ca2+ imaging of dorsal CA1 (dCA1) neurons in the stratum pyramidale (SP) in head-fixed mice performing a go-no go associative learning task to investigate how odor valence is temporally encoded in this area of the brain. We found that SP cells responded differentially to the rewarded or unrewarded odor. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual SP cells responding differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior. Our findings indicate that sequential activity of SP cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning. VIDEO ABSTRACT.
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Affiliation(s)
- Ming Ma
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Fabio Simoes de Souza
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Center for Mathematics, Computation and Cognition, Federal University of ABC, Sao Bernardo do Campo 09606-045, SP, Brazil
| | - Gregory L Futia
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sean R Anderson
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jose Riguero
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Daniel Tollin
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Arianna Gentile-Polese
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan P Platt
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kira Steinke
- Integrated Physiology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Naoki Hiratani
- Department of Neuroscience, Washington University, St. Louis, MO 63110, USA
| | - Emily A Gibson
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Diego Restrepo
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
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Ma M, Simoes de Souza F, Futia G, Anderson S, Riguero J, Tollin D, Gentile-Polese A, Platt J, Hiratani N, Gibson EA, Restrepo D. Decision-Making Time Cells in Hippocampal Dorsal CA1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.01.560382. [PMID: 37873178 PMCID: PMC10592611 DOI: 10.1101/2023.10.01.560382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Sequential neural dynamics encoded by "time cells" play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. In this manuscript, we used two-photon Ca2+ imaging of dorsal CA1 pyramidal neurons in head-fixed mice performing a go-no-go associative learning task. We found that pyramidal cells responded differentially to the rewarded or unrewarded stimuli. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual pyramidal cells that responded differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior indicating that sequential activity of pyramidal cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning.
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Affiliation(s)
- M. Ma
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- These authors contributed equally to this work
| | - F. Simoes de Souza
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Center for Mathematics, Computation and Cognition, Federal University of ABC, Sao Bernardo do Campo, SP, Brazil
- These authors contributed equally to this work
| | - G.L. Futia
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - S.R. Anderson
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - J. Riguero
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - D. Tollin
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - A. Gentile-Polese
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - J.P. Platt
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - N. Hiratani
- Department of Neuroscience, Washington University, St. Louis, MO 63110, USA
| | - E. A. Gibson
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - D. Restrepo
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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Cone I, Shouval HZ. Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife 2021; 10:63751. [PMID: 33734085 PMCID: PMC7972481 DOI: 10.7554/elife.63751] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/16/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic 'eligibility traces'. Before training, the network contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results.
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Affiliation(s)
- Ian Cone
- Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, United States.,Applied Physics, Rice University, Houston, TX, United States
| | - Harel Z Shouval
- Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, United States
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