A neural measure of interaural correlation based on variance in spike count

Abstract

Interaural Correlation (IAC) is related to variance in Interaural Time Difference (ITD) and Interaural Level Difference (ILD). While normalized IAC can account for behavioral performance in discrimination tasks, so can models directly employing this variance as a cue. Attempts at identifying a neural correlate of IAC discrimination have focused on changes in mean spike count, typically at the peaks of ITD tuning curves. We propose that IAC discrimination relies on variance in spike rates on the slope of neural tuning curves (for ITD). We developed a physiologically based hemispheric-balance model of IAC, where fluctuations in the ratio of activity between left- and right-brain serve as the detection cue to a reduction in IAC from unity, a ratio that is stimulus power invariant. Adjusting model parameters, we find that two orders of magnitude less activity is required in the variance-based, compared with mean spike-rate based, model, in order to achieve the same performance. This adjustment also revealed a necessary neural time integration of 10 ms, which is comparable with physiological estimates. The model was tested by recording neural responses from the midbrain of anesthetized guinea pigs to noise stimuli of various IAC.