Analysis of neural coding through quantization with an information-based distortion measure. Export

@article{dimitrov_03_analysis, 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.}, address = {Center for Computational Biology, Montana State University, Bozeman, MT 59717, USA.}, author = {Dimitrov, A. G. and Miller, J. P. and Gedeon, T. and Aldworth, Z. and Parker, A. E.}, citeulike-article-id = {2501366}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12613556}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12613556}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/12613556}, issn = {0954-898X}, journal = {Network: Computation in Neural Systems}, keywords = {bottleneck, coding, information}, month = {February}, number = {1}, pages = {151--176}, pdf = {dimitrov\_03\_analysis.pdf}, posted-at = {2008-03-10 15:26:03}, priority = {2}, title = {Analysis of neural coding through quantization with an information-based distortion measure.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12613556}, volume = {14}, year = {2003} }

TY - JOUR ID - dimitrov_03_analysis L3 - citeulike-article-id:2501366 N2 - 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. IS - 1 JF - Network: Computation in Neural Systems SN - 0954-898X AD - Center for Computational Biology, Montana State University, Bozeman, MT 59717, USA. EP - 176 TI - Analysis of neural coding through quantization with an information-based distortion measure. VL - 14 SP - 151 KW - bottleneck KW - coding KW - information AU - Dimitrov, AG AU - Miller, JP AU - Gedeon, T AU - Aldworth, Z AU - Parker, AE PY - 2003/February// UR - http://view.ncbi.nlm.nih.gov/pubmed/12613556 ER -