Lessons in digital estimation theory
Lessons in digital estimation theory
Elements of information theory
Elements of information theory
Spikes: exploring the neural code
Spikes: exploring the neural code
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
An Information-Theoretic Approach to Deciphering the Hippocampal Code
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Estimating a state-space model from point process observations
Neural Computation
Estimation, information and neural signals
Estimation, information and neural signals
Neural Computation
Spike train decoding without spike sorting
Neural Computation
Encoding and decoding spikes for dynamic stimuli
Neural Computation
Information in the nonstationary case
Neural Computation
Encoding through patterns: Regression tree-based neuronal population models
Neural Computation
Likelihood methods for point processes with refractoriness
Neural Computation
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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.