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Dynamic Analyses of Information Encoding in Neural Ensembles

barbieri{at}neurostat.mgh.harvard.edu, Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, U.S.A.

Loren M. Frank

loren{at}neurostat.mgh.harvard.edu, Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital; Division of Health, Sciences, and Technology, Harvard Medical School/MIT, Boston, MA 02114, U.S.A.

David P. Nguyen

dpnguyen{at}neurostat.mgh.harvard.edu, Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital; Division of Health, Sciences, and Technology, Harvard Medical School/MIT, Boston, MA 02114, U.S.A.

Michael C. Quirk

mquirk{at}ladyday.mwl.ai.mit.edu, Picower Center for Learning and Memory, Riken-MIT Neuroscience Research Center, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

Victor Solo

vic{at}nmr.mgh.harvard.edu, School of Electrical Engineering and Telecommunications, University of New South Wales, 2052, Sydney, Australia; Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, U.S.A.

Matthew A. Wilson

mwilson{at}mit.edu, Picower Center for Learning and Memory, Riken-MIT Neuroscience Research Center, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

Emery N. Brown

brown{at}neurostat.mgh.harvard.edu, Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital; Division of Health, Sciences, and Technology, Harvard Medical School/MIT, Boston, Massachusetts 02114-2698, U.S.A.

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to desig n decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

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