Journal of Computational Neuroscience

, Volume 33, Issue 2, pp 227–255 | Cite as

Uncovering spatial topology represented by rat hippocampal population neuronal codes

  • Zhe ChenEmail author
  • Fabian Kloosterman
  • Emery N. Brown
  • Matthew A. Wilson


Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden “spatial topology” represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.


Hidden Markov model Expectation-maximization Variational Bayesian inference Place cells Population codes Spatial topology Force-based algorithm 



The work was supported in part by the NIH Grant DP1-OD003646 (E.N.B.) and MH061976 (M.A.W.). We thank Stuart Layton for providing the linear track experimental data used in the paper.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Zhe Chen
    • 1
    • 2
    Email author
  • Fabian Kloosterman
    • 3
  • Emery N. Brown
    • 1
    • 4
  • Matthew A. Wilson
    • 3
  1. 1.Neuroscience Statistics Research Lab, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  2. 2.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of Brain and Cognitive Sciences and Picower Institute for Learning and MemoryMassachusetts Institute of TechnologyCambridgeUSA
  4. 4.Department of Brain and Cognitive Sciences and Harvard-MIT Division of Health and Science TechnologyMassachusetts Institute of TechnologyCambridgeUSA

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