Changes of AI receptive fields with sound density

J Neurophysiol. 2002 Dec;88(6):3409-20. doi: 10.1152/jn.00233.2002.


Primates engage in auditory behaviors under a broad range of signal-to-noise conditions. In this study, optimal linear receptive fields were measured in alert primate primary auditory cortex (A1) in response to stimuli that vary in spectrotemporal density. As density increased, A1 excitatory receptive fields systematically changed. Receptive field sensitivity, expressed as the expected change in firing rate after a tone pip onset, decreased by an order of magnitude. Spectral selectivity more than doubled. Inhibitory subfields, which were rarely recorded at low sound densities, emerged at higher sound densities. The ratio of excitatory to inhibitory population strength changed from 14.4:1 to 1.4:1. At low sound densities, the sound associated with the evocation of an action potential from an A1 neuron was broad in spectrum and time. At high sound densities, a spike-evoking sound was more likely to be a spectral or temporal edge and was narrower in time and frequency range. Receptive fields were used to predict responses to a novel high-noise-density stimulus. The predictions were highly correlated with the actual responses to the 2-s complex sound excerpt. The structure of prediction failures revealed that neurons with prominent inhibitory fields had relatively poor linear predictions. Further, the finding that stochastic variance is limiting in prediction even after averaging 150 repetitions means that high-fidelity representations of simple sounds in A1 must be distributed over at least hundreds of neurons. Auditory context alters A1 responses across multiple parameter spaces; this presents a challenge for reconstructing neural codes.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Acoustic Stimulation / methods
  • Action Potentials
  • Animals
  • Aotidae
  • Auditory Cortex / physiology*
  • Electrophysiology
  • Forecasting
  • Sound*
  • Stochastic Processes
  • Time Factors