The formation of neural codes in the hippocampus: trace conditioning as a prototypical paradigm for studying the random recoding hypothesis.
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.
Levy
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WB
Department of Neurosurgery, University of Virginia, P.O. Box 800420, Charlottesville, VA 22908, USA. wbl@virginia.edu
Sanyal
A
A
Rodriguez
P
P
Sullivan
D W
DW
Wu
X B
XB
D000465:Algorithms; D003214:Conditioning, Classical / Q000502:physiology*; D006624:Hippocampus / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*