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To process the rich temporal structure of their acoustic environment, organisms have to integrate information over time into an appropriate neural response. Previous studies have addressed the modulation of responses of auditory neurons to a current sound in dependence of the immediate stimulation history, but a quantitative analysis of this important computational process has been missing. In this study, we analyzed temporal integration of information in the spike output of 122 single neurons in primary auditory cortex (A1) of four awake ferrets in response to random tone sequences. We quantified the information contained in the responses about both current and preceding sounds in two ways: by estimating directly the mutual information between stimulus and response, and by training linear classifiers to decode information about the stimulus from the neural response. We found that 1) many neurons conveyed a significant amount of information not only about the current tone but also simultaneously about the previous tone, 2) the neural response to tone sequences was a nonlinear combination of responses to the tones in isolation, and 3) nevertheless, much of the information about current and previous tones could be extracted by linear decoders. Furthermore, our analysis of these experimental data shows that methods from information theory and the application of standard machine learning methods for extracting specific information yield quite similar results.

A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons