Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. 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 place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.T he receptive fields of neurons are dynamic; that is, their responses to relevant stimuli change with experience. Experience-dependent change or plasticity has been documented in a number of brain regions (1-5). For example, in the cat visual system, retinal lesions lead to reorganization of cortical topography (3). Peripheral nerve sectioning can alter substantially the receptive fields of neurons in monkey somatosensory and motor cortices (6, 7). Similarly, the directional tuning of neural receptive fields in monkey motor cortex changes as the animal learns to compensate for an externally applied force field while moving a manipulandum (8). In the rat hippocampus, the system we study here, the pyramidal neurons in the CA1 region have spatial receptive fields. As a rat executes a behavioral task, a given CA1 neuron fires only in a restricted region of the experimental environment, termed the cell's spatial or place receptive field (9). Place fields change in a reliable manner as the animal executes its task (5, 10). When the experimental environment is a linear track, these spatial receptive fields on average migrate and skew in the direction opposite the cell's preferred direction of firing relative to the animal's movement and increase in scale and maximum firing rate (5, 10). Because receptive field plasticity is a characteristic of many neural systems, analysis of these dynamics from experimental measurements is crucial for understanding how different br...