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J Neurophysiol (October 1, 2003). doi:10.1152/jn.00022.2003
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Submitted on January 10, 2003
Accepted on September 26, 2003

Information content of auditory cortical responses to time-varying acoustic stimuli

Thomas Lu1 and Xiaoqin Wang1*

1 Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

* To whom correspondence should be addressed. E-mail: xwang{at}bme.jhu.edu.

The present study explores the issue of cortical coding by spike count and timing using statistical and information theoretic methods. We have shown in previous studies that neurons in the auditory cortex of awake primates have an abundance of sustained discharges that could represent time-varying signals by temporal discharge patterns or mean firing rates. In particular, we found that a subpopulation of neurons can encode rapidly occurring sounds, such as a click train, with discharges that are not synchronized to individual stimulus events, suggesting a temporal-to-rate transformation. We investigated whether there were stimulus-specific temporal patterns embedded in these seemingly random spike times. Furthermore, we quantitatively analyzed the precision of spike timing at stimulus onset and during ongoing acoustic stimulation. The main findings are 1) Temporal and rate codes may operate at separate stimulus domains, or encode the same stimulus domain in parallel via different neuronal populations. 2) Spike timing was crucial to encode stimulus periodicity in "synchronized" neurons. 3) "Non-synchronized" neurons showed little stimulus-specific spike timing information in their responses to time-varying signals. Such responses therefore represent processed (instead of preserved) information in the auditory cortex. 4) Spike timing on the occurrence of acoustic events was more precise at the first event than at successive ones, and more precise with sparsely distributed events (longer time intervals between events) than with densely packed events. These results indicate that auditory cortical neurons mark sparse acoustic events (or onsets) with precise spike timing and transform rapidly occurring acoustic events into firing rate-based representations.




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