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Dynamic analysis of neural encoding by point process adaptive filtering.
Neural Comput. 2004 May; 16(5):971-98.NC

Abstract

Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.

Authors+Show Affiliations

Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, and Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Cambridge, MA, USA. tzvi@neurostat.mgh.harvard.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

15070506

Citation

Eden, Uri T., et al. "Dynamic Analysis of Neural Encoding By Point Process Adaptive Filtering." Neural Computation, vol. 16, no. 5, 2004, pp. 971-98.
Eden UT, Frank LM, Barbieri R, et al. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 2004;16(5):971-98.
Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16(5), 971-98.
Eden UT, et al. Dynamic Analysis of Neural Encoding By Point Process Adaptive Filtering. Neural Comput. 2004;16(5):971-98. PubMed PMID: 15070506.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dynamic analysis of neural encoding by point process adaptive filtering. AU - Eden,Uri T, AU - Frank,Loren M, AU - Barbieri,Riccardo, AU - Solo,Victor, AU - Brown,Emery N, PY - 2004/4/9/pubmed PY - 2004/5/12/medline PY - 2004/4/9/entrez SP - 971 EP - 98 JF - Neural computation JO - Neural Comput VL - 16 IS - 5 N2 - Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale. SN - 0899-7667 UR - https://www.unboundmedicine.com/medline/citation/15070506/Dynamic_analysis_of_neural_encoding_by_point_process_adaptive_filtering_ L2 - https://direct.mit.edu/neco/article-lookup/doi/10.1162/089976604773135069 DB - PRIME DP - Unbound Medicine ER -