Journal article
Analysis of neural coding through quantization with an information-based distortion measure
Network (Bristol), Vol.14(1), pp.151-176
02/2003
Handle:
https://hdl.handle.net/2376/117747
PMID: 12613556
Abstract
We discuss an analytical approach through which the neural symbols and corresponding stimulus space of a neuron or neural ensemble can be discovered simultaneously and quantitatively, making few assumptions about the nature of the code or relevant features. The basis for this approach is to conceptualize a neural coding scheme as a collection of stimulus-response classes akin to a dictionary or 'codebook', with each class corresponding to a spike pattern 'codeword' and its corresponding stimulus feature in the codebook. The neural codebook is derived by quantizing the neural responses into a small reproduction set, and optimizing the quantization to minimize an information-based distortion function. We apply this approach to the analysis of coding in sensory interneurons of a simple invertebrate sensory system. For a simple sensory characteristic (tuning curve), we demonstrate a case for which the classical definition of tuning does not describe adequately the performance of the cell studied. Considering a more involved sensory operation (sensory discrimination), we also show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.
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Details
- Title
- Analysis of neural coding through quantization with an information-based distortion measure
- Creators
- Alexander G Dimitrov - Center for Computational Biology, Montana State University, Bozeman, MT 59717, USAJohn P MillerTomás GedeonZane AldworthAlbert E Parker
- Publication Details
- Network (Bristol), Vol.14(1), pp.151-176
- Academic Unit
- Mathematics and Statistics, Department of
- Publisher
- England
- Grant note
- MH57179 / NIMH NIH HHS MH12159 / NIMH NIH HHS
- Identifiers
- 99900548031001842
- Language
- English
- Resource Type
- Journal article