Literature DB >> 15965710

The formation of neural codes in the hippocampus: trace conditioning as a prototypical paradigm for studying the random recoding hypothesis.

W B Levy1, A Sanyal, P Rodriguez, D W Sullivan, X B Wu.   

Abstract

The trace version of classical conditioning is used as a prototypical hippocampal-dependent task to study the recoding sequence prediction theory of hippocampal function. This theory conjectures that the hippocampus is a random recoder of sequences and that, once formed, the neuronal codes are suitable for prediction. As such, a trace conditioning paradigm, which requires a timely prediction, seems by far the simplest of the behaviorally-relevant paradigms for studying hippocampal recoding. Parameters that affect the formation of these random codes include the temporal aspects of the behavioral/cognitive paradigm and certain basic characteristics of hippocampal region CA3 anatomy and physiology such as connectivity and activity. Here we describe some of the dynamics of code formation and describe how biological and paradigmatic parameters affect the neural codes that are formed. In addition to a backward cascade of coding neurons, we point out, for the first time, a higher-order dynamic growing out of the backward cascade-a particular forward and backward stabilization of codes as training progresses. We also observe that there is a performance compromise involved in the setting of activity levels due to the existence of three behavioral failure modes. Each of these behavioral failure modes exists in the computational model and, presumably, natural selection produced the compromise performance observed by psychologists. Thus, examining the parametric sensitivities of the codes and their dynamic formation gives insight into the constraints on natural computation and into the computational compromises ensuing from these constraints.

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Year:  2005        PMID: 15965710     DOI: 10.1007/s00422-005-0568-9

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  11 in total

1.  Memory retrieval time and memory capacity of the CA3 network: role of gamma frequency oscillations.

Authors:  Licurgo de Almeida; Marco Idiart; John E Lisman
Journal:  Learn Mem       Date:  2007-11-14       Impact factor: 2.460

Review 2.  Towards a unified model of pavlovian conditioning: short review of trace conditioning models.

Authors:  V I Kryukov
Journal:  Cogn Neurodyn       Date:  2012-02-22       Impact factor: 5.082

Review 3.  Prediction, sequences and the hippocampus.

Authors:  John Lisman; A D Redish
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2009-05-12       Impact factor: 6.237

4.  Hippocampal episode fields develop with learning.

Authors:  Patrick R Gill; Sheri J Y Mizumori; David M Smith
Journal:  Hippocampus       Date:  2010-07-21       Impact factor: 3.899

5.  A Neural Mechanism for Reward Discounting: Insights from Modeling Hippocampal-Striatal Interactions.

Authors:  Patryk A Laurent
Journal:  Cognit Comput       Date:  2013-03-01       Impact factor: 5.418

6.  Neuronal dynamics during the learning of trace conditioning in a CA3 model of hippocampal function.

Authors:  Blake T Thomas; William B Levy
Journal:  Cogn Neurodyn       Date:  2013-10-22       Impact factor: 5.082

7.  CA1 cell activity sequences emerge after reorganization of network correlation structure during associative learning.

Authors:  Mehrab N Modi; Ashesh K Dhawale; Upinder S Bhalla
Journal:  Elife       Date:  2014-03-25       Impact factor: 8.140

8.  A hippocampal model predicts a fluctuating phase transition when learning certain trace conditioning paradigms.

Authors:  Andrew G Howe; William B Levy
Journal:  Cogn Neurodyn       Date:  2007-01-25       Impact factor: 5.082

Review 9.  A unified framework for addiction: vulnerabilities in the decision process.

Authors:  A David Redish; Steve Jensen; Adam Johnson
Journal:  Behav Brain Sci       Date:  2008-08       Impact factor: 21.357

10.  Temporal-difference reinforcement learning with distributed representations.

Authors:  Zeb Kurth-Nelson; A David Redish
Journal:  PLoS One       Date:  2009-10-20       Impact factor: 3.240

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