Abstract
Interpreting messages encoded in single neuronal responses requires knowing which features of the responses carry information. That the number of spikes is an important part of the code has long been obvious. In recent years, it has been shown that modulation of the firing rate with about 25 ms precision carries information that is not available from the total number of spikes across the whole response. It has been proposed that patterns of exactly timed (1 ms precision) spikes, such as repeating triplets or quadruplets, might carry information that is not available from knowing about spike count and rate modulation. A model using the spike count distribution, the low pass filtered PSTH (bandwidth below 30 Hz), and, to a small degree, the interspike interval distribution predicts the numbers and types of exactly-timed triplets and quadruplets that are indistinguishable from those found in the data. From this it can be concluded that the coarse (<30 Hz) sequential correlation structure over time gives rise to the exactly timed patterns present in the recorded spike trains. Because the coarse temporal structure predicts the fine temporal structure, the information carried by the fine temporal structure must be completely redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.
Highlights
Interpreting the information encoded in single neuronal responses requires knowing which response features carry information
The results reviewed above show that accurately describing spike trains requires specifying only (a)the spike count distribution, (b)the peristimulus time histogram (PSTH), and (c)the interval histogram
If they completely describe single neuronal responses, these features contain all of the information that is available from those responses, no matter what representation is chosen
Summary
Interpreting the information encoded in single neuronal responses requires knowing which response features carry information. One approach is to identify the smallest number of parameters that are needed to represent all aspects of neuronal responses encoding information. We are interested less in the spike train itself, than in its role in transmitting information Only those aspects of the response that carry unique information need be included. The less the responses to different stimuli overlap, the smaller the chance that more than one stimulus elicited a particular response, and the higher the information. The more the responses to different stimuli overlap, the more difficult it is to determine which stimulus elicited a particular response, and the lower the information. Our minimal description of neuronal responses must include both the average responses to the stimuli and their variabilities, namely, we must know the distribution of responses to each stimulus. Often we want to know whether adding an element to the representation of the response (that is, increasing the dimension of the code) adds information
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