Tracking modulation of neural encoding in the natural visual world

Traditional approaches to characterizing the transformation from a visual stimulus to neural response assume that 1) the stimulus is stationary and uncorrelated, and 2) the functional properties of the encoding do not change over time. However, recent studies of sensory function under natural stimulus conditions have demonstrated important features of neural encoding that are in violation of these assumptions. Characterizing the stimulus/response mapping in a natural setting demands a more realistic model of sensory encoding in which stimuli of arbitrary complexity are adaptively filtered into a neural response. To identify the stimulus/response mapping in this context, a new analytic technique must be developed. In this paper, a point process extended recursive least-squares (ERLS) approach to receptive field (RF) estimation is detailed. Simulated and experimental neural responses are used to demonstrate the ability of the ERLS technique to estimate RFs from responses to complex natural stimuli and track adaptation of receptive field properties during a single trial. The ERLS technique lends tremendous flexibility to experimental design, facilitating the investigation of sensory function in the natural environment.