A STATISTICAL PARADIGM FOR NEURAL SPIKE TRAIN DECODING APPLIED TO POSITION PREDICTION FROM ENSEMBLE FIRING PATTERNS OF RAT HIPPOCAMPAL PLACE CELLS

The problem of predicting the position of a freely foraging rat based on the ensemble firing patterns of place cells recorded from the CA1 r egion of its hippocampus is used to develop a two-stage statistical pa radigm for neural spike train decoding. In the first, or encoding stag e, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of the animal's positio n 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 Bayesi an statistical paradigm is used to derive a nonlinear recursive causal filter algorithm for predicting the position of the animal from the p lace cell ensemble firing patterns. The algebra of the decoding algori thm defines an explicit map of the discrete spike trains into the posi tion prediction. The confidence regions for the position predictions q uantify spike train information in terms of the most probable location s of the animal given the ensemble firing pattern. Under our inhomogen eous Poisson model position was a three to five times stronger modulat or of the place cell spiking activity than theta phase in an open circ ular 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) c m. Our statistical paradigm provides a reliable approach for quantifyi ng the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.