Information Through a Spiking Neuron

Abstract

While it is generally agreed that neurons transmit information about their synaptic inputs through spike trains, the code by which this information is transmitted is not well understood. An upper bound on the information encoded is obtained by hypothesizing that the precise timing of each spike conveys information. Here we develop a general approach to quantifying the information carried by spike trains under this hypothesis, and apply it to the leaky integrate-and-fire (IF) model of neuronal dynamics. We formu(cid:173) late the problem in terms of the probability distribution peT) of interspike intervals (ISIs), assuming that spikes are detected with arbitrary but finite temporal resolution . In the absence of added noise, all the variability in the ISIs could encode information, and the information rate is simply the entropy of the lSI distribution, H (T) = (-p(T) log2 p(T)}, times the spike rate. H (T) thus pro(cid:173) vides an exact expression for the information rate. The methods developed here can be used to determine experimentally the infor(cid:173) mation carried by spike trains, even when the lower bound of the information rate provided by the stimulus reconstruction method is not tight. In a preliminary series of experiments, we have used these methods to estimate information rates of hippocampal neu(cid:173) rons in slice in response to somatic current injection. These pilot experiments suggest information rates as high as 6.3 bits/spike.