An analysis of neural receptive field plasticity by point process adaptivefiltering

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of re ceptive field plasticity from experimental measurements is crucial for unde rstanding how neural systems adapt their representations of relevant biolog ical information. Current analysis methods using histogram estimates of spi ke rate functions in nonoverlapping temporal windows do not track the evolu tion of receptive field plasticity on a fine time scale. Adaptive signal pr ocessing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive f ilter algorithm for tracking neural receptive field plasticity based on poi nt process models of spike train activity. We derive an instantaneous steep est descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point pro cess adaptive filter algorithm in a study of spatial (place) receptive fiel d properties of simulated and actual spike train data from rat CA1 hippocam pal neurons. A stability analysis of the algorithm is sketched in the Appen dix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal pla ce fields in a rat running on a linear track. Point process adaptive filter ing offers an analytic method for studying the dynamics of neural receptive fields.