A dynamic network model of temporal receptive fields in primary auditory cortex

• DOI: 10.1371/journal.pcbi.1006618
• Publication Date: 2019-05-06T18:37:05Z
• Authors: Monzilur Rahman; Ben D. B. Willmore; Andrew J. King; Nicol S. Harper
• Keywords: Stimulus (psychology); Natural sounds; Time constant; Neurophysiology

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in spectro-temporal receptive field (STRF). We propose an alternative model for the encoding we apply to extracellular recordings primary auditory cortex anaesthetized ferrets. For linear-non-linear STRF (LN model) achieve high level performance predicting single unit neural responses natural sounds cortex, found that it necessary include time delays going back at least 200 ms past. This unrealistic biological delay lines. therefore asked how much this dependence on history can instead be explained by dynamical aspects neurons. constructed neural-network whose output weighted sum units are determined dynamic firing-rate equation. The aspect performs low-pass filtering each unit's response, providing exponentially decaying memory constant individual unit. find network (DNet) model, when fitted data STRFs only 25 duration, prediction held-out dataset comparable best performing LN with duration. These findings suggest integration due membrane constants or other exponentially-decaying processes may underlie linear temporal fields beyond ms.