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Temporal Encoding in Nervous Systems: a Rigorous Definition

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Specialties Biology
Neurology
Date 1995 Jun 1
PMID 8521284
Citations 119
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Abstract

We propose a rigorous definition for the term temporal encoding as it is applied to schemes for the representation of information within patterns of neuronal action potentials, and distinguish temporal encoding schemes from those based on window-averaged mean rate encoding. The definition relies on the identification of an encoding time window, defined as the duration of a neuron's spike train assumed to correspond to a single symbol in the neural code. The duration of the encoding time window is dictated by the time scale of the information being encoded. We distinguish between the concepts of the encoding time window and the integration time window, the latter of which is defined as the duration of a stimulus signal that affects the response of the neuron. We note that the duration of the encoding and integration windows might be significantly different. We also present objective, experimentally assessable criteria for identifying neurons and neuronal ensembles that utilize temporal encoding to any significant extent. The definitions and criteria are made rigorous within the contexts of several commonly used analytical approaches, including the stimulus reconstruction analysis technique. Several examples are presented to illustrate the distinctions between and relative capabilities of rate encoding and temporal encoding schemes. We also distinguish our usage of temporal encoding from the term temporal coding, which is commonly used in reference to the representation of information about the timing of events by rate encoding schemes.

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References
1.
Miller J, Jacobs G, Theunissen F . Representation of sensory information in the cricket cercal sensory system. I. Response properties of the primary interneurons. J Neurophysiol. 1991; 66(5):1680-9. DOI: 10.1152/jn.1991.66.5.1680. View

2.
Optican L, Richmond B . Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. J Neurophysiol. 1987; 57(1):162-78. DOI: 10.1152/jn.1987.57.1.162. View

3.
McClurkin J, Optican L, Richmond B, Gawne T . Concurrent processing and complexity of temporally encoded neuronal messages in visual perception. Science. 1991; 253(5020):675-7. DOI: 10.1126/science.1908118. View

4.
Gooler D, Feng A . Temporal coding in the frog auditory midbrain: the influence of duration and rise-fall time on the processing of complex amplitude-modulated stimuli. J Neurophysiol. 1992; 67(1):1-22. DOI: 10.1152/jn.1992.67.1.1. View

5.
Korenberg M, Hunter I . The identification of nonlinear biological systems: Wiener kernel approaches. Ann Biomed Eng. 1990; 18(6):629-54. DOI: 10.1007/BF02368452. View