Interpreting the information encoded in single neuronal responses requires knowing which response features carry information. Despite a great deal of study, however, it is still not completely train which response features are important. To describe a neuronal response completely we must specify the arrival time of each spike. However, we are interested less in the spike train itself than in its role in transmitting information. Therefore, only those aspects of the response that carry unique information need be included. Over the past year we have shown that all of the information carried by neuronal spike trains requires specifying only the spike count distribution (which is approximately truncated Gaussian),the variation in firing rate with a bandwidth of less than 30 Hz, the equivalent of measuring spike counts in 30 ms wide bins), and the interval histogram. If these features completely describe single neuronal responses, they contain all of the information available from those responses, no matter what representation is chosen. The reason the spike count distribution (that is, knowing how many times each spike count occurs) is so important is that the temporal coding depends almost completely on the spike count. Intuitively this seems clear when we realize that the more spikes that are present, the richer the potential temporal code. Thus, the influence of variation in the number of spikes that occurs with successive presentations of a stimlus must be properly taken into account when estimates of neuronal coding are made. Given this knowledge of the response we are also able to estimate accurately the maximum amount of information that can ever be carried by a single neuron, and how that maximum can be achieved. This maximum is no more that twice the amount of information that can be carried by knowing only the spike count. This study also suggests strongly that, although it is possible for this maximum to be reached for one neuron, it is not possible for all neurons within the visual system at least to reach their maxima simultaneously. We conclude that the spike trains are consistent with a stochastic process generating all ofthespikes, and that serial correlation on a broad time scale (spike count distribution and the rate changes over relatively long time) rise to the fine temporal structures seen in the data. Thus, the existence of structure at fine time scales does not imply control at fine timescales. It is always necessary to account for the influence of correlations over long periods on precisely timed patterns of any type found in spike trains. When the fine temporal structure is predicted from the coarse temporal structure, it can carry no unique information and then, for assessing the information carried, only the coarse structure need be described. - single neurons, perception, memory, cognition, neural codes, multiple neurons
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