A Computational Model of the Underlying Mechanisms of Temporal Coding in the Auditory Cortex

Abstract

In primary auditory cortex, slowly repeated acoustic events are represented temporally by phase-locked activity of single neurons. Single-unit studies in awake marmosets (Callithrix jacchus) have shown that a sub-population of these neurons also monotonically increase or decrease their average discharge rate during stimulus presentation for higher repetition rates. Building on a computational single-neuron model that generates phase-locked responses with stimulus evoked excitation followed by strong inhibition, we find that stimulus-evoked short-term depression is sufficient to produce synchronized monotonic positive and negative responses to slowly repeated stimuli. By exploring model robustness and comparing it to other models for adaptation to such stimuli, we conclude that short-term depression best explains our observations in single-unit recordings in awake marmosets. Using this model, we emulated how single neurons could encode and decode multiple aspects of an acoustic stimuli with the monotonic positive and negative encoding of a given stimulus feature. Together, our results show that a simple biophysical mechanism in single neurons can allow a more complex encoding and decoding of acoustic stimuli.