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Temporal Coding of Time-Varying Stimuli

shamir{at}bu.edu Hearing Research Center and Center for BioDynamics, Department of Biomedical Engineering, and Department of Mathematics, Boston University, Boston, MA 02215, U.S.A.

Kamal Sen

kamalsen{at}bu.edu Hearing Research Center and Center for BioDynamics, Department of Biomedical Engineering, Boston University, Boston, MA 02115, U.S.A.

H. Steven Colburn

colburn{at}bu.edu Hearing Research Center and Center for BioDynamics, Department of Biomedical Engineering, Boston University, Boston, MA 02115, U.S.A.

Temporal structure is an inherent property of various sensory inputs and motor outputs of the brain. For example, auditory stimuli are defined by the sound waveform. Temporal structure is also an important feature of certain visual stimuli, for example, the image on the retina of a fly during flight. In many cases, this temporal structure of the stimulus is being represented by a time-dependent neuronal activity that is locked to certain features of the stimulus. Here, we study the information capacity of the temporal code. In particular we are interested in the following questions. First, how does the information content of the code depend on the observation time of the cell's response, and what is the effect of temporal noise correlations on this information capacity? Second, what is the effect on the information content of reading the code with a finite temporal resolution for the neural response? We address these questions in the framework of a statistical model for the neuronal temporal response to a time-varying stimulus in a two-Alternative forced-choice paradigm. We show that information content of the temporal response scales linearly with the overall time of the response, even in the presence of temporal noise correlations. More precisely, we find that positive temporal noise correlations have a scaling effect that decreases the information content. Nevertheless, the information content of the response continues to scale linearly with the observation time. We further show that finite temporal resolution is sufficient for obtaining most of the information from the cell's response. This finite timescale is related to the response properties of the cell.