Receptive field for dorsal cochlear nucleus neurons at multiple sound levels

Sharba Bandyopadhyay, Lina A.J. Reiss, Eric D. Young

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Neurons in the dorsal cochlear nucleus (DCN) exhibit nonlinearities in spectral processing, which make it difficult to predict the neurons' responses to stimuli. Here, we consider two possible sources of nonlinearity: nonmonotonic responses as sound level increases due to inhibition and interactions between frequency components. A spectral weighting function model of rate responses is used; the model approximates the neuron's rate response as a weighted sum of the frequency components of the stimulus plus a second-order sum that captures interactions between frequencies. Such models approximate DCN neurons well at low spectral contrast, i.e., when the SD (contrast) of the stimulus spectrum is limited to 3 dB. This model is compared with a first-order sum with weights that are explicit functions of sound level, so that the lowcontrast model is extended to spectral contrasts of 12 dB, the range of natural stimuli. The sound-level- dependent weights improve prediction performance at large spectral contrast. However, the interactions between frequencies, represented as second-order terms, are more important at low spectral contrast. The level-dependent model is shown to predict previously described patterns of responses to spectral edges, showing that small changes in the inhibitory components of the receptive field can produce large changes in the responses of the neuron to features of natural stimuli. These results provide an effective way of characterizing nonlinear auditory neurons incorporating stimulus-dependent sensitivity changes. Such models could be used for neurons in other sensory systems that show similar effects.

Original languageEnglish (US)
Pages (from-to)3505-3515
Number of pages11
JournalJournal of neurophysiology
Issue number6
StatePublished - Dec 2007
Externally publishedYes

ASJC Scopus subject areas

  • General Neuroscience
  • Physiology


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