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The Journal of Neuroscience, September 15, 1998, 18(18):7411-7425
A Statistical Paradigm for Neural Spike Train Decoding Applied to
Position Prediction from Ensemble Firing Patterns of Rat Hippocampal
Place Cells
Emery N.
Brown1,
Loren
M.
Frank2,
Dengda
Tang1,
Michael C.
Quirk2, and
Matthew A.
Wilson2
1 Statistics Research Laboratory, Department of
Anesthesia and Critical Care, Harvard Medical School, Massachusetts
General Hospital, Boston, Massachusetts 02114-2698, and
2 Department of Brain and Cognitive Sciences, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139
The problem of predicting the position of a freely foraging rat
based on the ensemble firing patterns of place cells recorded from the
CA1 region of its hippocampus is used to develop a two-stage statistical paradigm for neural spike train decoding. In the first, or
encoding stage, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of
the animal's position in space and phase of its theta rhythm. The
animal's path is modeled as a Gaussian random walk. In the second, or
decoding stage, a Bayesian statistical paradigm is used to derive a
nonlinear recursive causal filter algorithm for predicting the position
of the animal from the place cell ensemble firing patterns. The algebra
of the decoding algorithm defines an explicit map of the discrete spike
trains into the position prediction. The confidence regions for the
position predictions quantify spike train information in terms of the
most probable locations of the animal given the ensemble firing
pattern. Under our inhomogeneous Poisson model position was a three to
five times stronger modulator of the place cell spiking activity than
theta phase in an open circular environment. For animal 1 (2) the
median decoding error based on 34 (33) place cells recorded during 10 min of foraging was 8.0 (7.7) cm. Our statistical paradigm provides a
reliable approach for quantifying the spatial information in the
ensemble place cell firing patterns and defines a generally applicable
framework for studying information encoding in neural systems.
Key words:
hippocampal place cells; Bayesian statistics; information
encoding; decoding algorithm; nonlinear recursive filter; random walk; inhomogeneous Poisson process; point process.
Copyright © 1998 Society for Neuroscience 0270-6474/98/18187411-15$05.00/0
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