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An analysis of neural receptive field plasticity by point process adaptive filtering.
Proc Natl Acad Sci U S A. 2001 Oct 09; 98(21):12261-6.PN

Abstract

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.

Authors+Show Affiliations

Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA. brown@neurostat.mgh.harvard.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

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

Language

eng

PubMed ID

11593043

Citation

Brown, E N., et al. "An Analysis of Neural Receptive Field Plasticity By Point Process Adaptive Filtering." Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 21, 2001, pp. 12261-6.
Brown EN, Nguyen DP, Frank LM, et al. An analysis of neural receptive field plasticity by point process adaptive filtering. Proc Natl Acad Sci U S A. 2001;98(21):12261-6.
Brown, E. N., Nguyen, D. P., Frank, L. M., Wilson, M. A., & Solo, V. (2001). An analysis of neural receptive field plasticity by point process adaptive filtering. Proceedings of the National Academy of Sciences of the United States of America, 98(21), 12261-6.
Brown EN, et al. An Analysis of Neural Receptive Field Plasticity By Point Process Adaptive Filtering. Proc Natl Acad Sci U S A. 2001 Oct 9;98(21):12261-6. PubMed PMID: 11593043.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An analysis of neural receptive field plasticity by point process adaptive filtering. AU - Brown,E N, AU - Nguyen,D P, AU - Frank,L M, AU - Wilson,M A, AU - Solo,V, PY - 2001/10/11/pubmed PY - 2002/1/5/medline PY - 2001/10/11/entrez KW - Non-programmatic SP - 12261 EP - 6 JF - Proceedings of the National Academy of Sciences of the United States of America JO - Proc Natl Acad Sci U S A VL - 98 IS - 21 N2 - Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. SN - 0027-8424 UR - https://www.unboundmedicine.com/medline/citation/11593043/An_analysis_of_neural_receptive_field_plasticity_by_point_process_adaptive_filtering_ L2 - http://www.pnas.org/cgi/pmidlookup?view=long&pmid=11593043 DB - PRIME DP - Unbound Medicine ER -