Computer graphics (2nd ed.): C version
Computer graphics (2nd ed.): C version
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
2005 Special issue: Hippocampal mechanisms for the context-dependent retrieval of episodes
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
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Firing activity from neural ensembles in rat hippocampus has been previously used to determine an animal's position in an open environment and separately to predict future behavioral decisions. However, a unified statistical procedure to combine information about position and behavior in environments with complex topological features from ensemble hippocampal activity has yet to be described. Here we present a two-stage computational framework that uses point process filters to simultaneously estimate the animal's location and predict future behavior from ensemble neural spiking activity. First, in the encoding stage, we linearized a two-dimensional T-maze, and used spline-based generalized linear models to characterize the place-field structure of different neurons. All of these neurons displayed highly specific position-dependent firing, which frequently had several peaks at multiple locations along the maze. When the rat was at the stem of the T-maze, the firing activity of several of these neurons also varied significantly as a function of the direction it would turn at the decision point, as detected by ANOVA. Second, in the decoding stage, we developed a state-space model for the animal's movement along a T-maze and used point process filters to accurately reconstruct both the location of the animal and the probability of the next decision. The filter yielded exact full posterior densities that were highly nongaussian and often multimodal. Our computational framework provides a reliable approach for characterizing and extracting information from ensembles of neurons with spatially specific context or task-dependent firing activity.