Bayesian decoding algorithm for analysis of information encoding in neural ensembles

Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience. In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. 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 analyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from a rat foraging in an open circular environment. The median decoding error during 10 minutes of open foraging was 5.5 cm, and the true coverage probability for 0.95 confidence regions was 0.75 using 32 neurons. These findings improve significantly on our previous results and suggest an approach to reading dynamically information represented in ensemble neural spiking activity.