We demonstrate that the information contained in the spike occurrence times of a population of neurons can be broken up into a series of terms, each reflecting something about potential coding mechanisms. This is possible in the coding regime in which few spikes are emitted in the relevant time window. This approach allows us to study the additional information contributed by spike timing beyond that present in the spike counts and to examine the contributions to the whole information of different statistical properties of spike trains, such as firing rates and correlation functions. It thus forms the basis for a new quantitative procedure for analyzing simultaneous multiple neuron recordings and provides theoretical constraints on neural coding strategies. We find a transition between two coding regimes, depending on the size of the relevant observation timescale. For time windows shorter than the timescale of the stimulus-induced response fluctuations, there exists a spike count coding phase, in which the purely temporal information is of third order in time. For time windows much longer than the characteristic timescale, there can be additional timing information of first order, leading to a temporal coding phase in which timing information may affect the instantaneous information rate. In this new framework, we study the relative contributions of the dynamic firing rate and correlation variables to the full temporal information, the interaction of signal and noise correlations in temporal coding, synergy between spikes and between cells, and the effect of refractoriness. We illustrate the utility of the technique by analyzing a few cells from the rat barrel cortex.

Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.

Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.

We sought to determine the neural code(s) for frequency discrimination of vibrotactile stimuli. We tested five possible candidate codes by analyzing the responses of single neurons recorded in primary somatosensory cortex of trained monkeys while they discriminated between two consecutive vibrotactile stimuli. Differences in the frequency of two stimuli could be discriminated using information from (i) time intervals between spikes, (ii) average spiking rate during each stimulus, (iii) absolute number of spikes elicited by each stimulus, (iv) average rate of bursts of spikes or (v) absolute number of spike bursts elicited by each stimulus. However, only a spike count code, in which spikes are integrated over a time window that has most of its mass in the first 250 ms of each stimulus period, covaried with behavior on a trial-by-trial basis, was consistent with psychophysical biases induced by manipulation of stimulus duration, and produced neurometric discrimination thresholds similar to behavioral psychophysical thresholds.

In the primate primary visual cortex (V1), the significance of individual action potentials has been difficult to determine, particularly in light of the considerable trial-to-trial variability of responses to visual stimuli. We show here that the information conveyed by an action potential depends on the duration of the immediately preceding interspike interval (ISI). The interspike intervals can be grouped into several different classes on the basis of reproducible features in the interspike interval histograms. Spikes in different classes bear different relationships to the visual stimulus, both qualitatively (in terms of the average stimulus preceding each spike) and quantitatively (in terms of the amount of information encoded per spike and per second). Spikes preceded by very short intervals (3 msec or less) convey information most efficiently and contribute disproportionately to the overall receptive-field properties of the neuron. Overall, V1 neurons can transmit between 5 and 30 bits of information per second in response to rapidly varying, pseudorandom stimuli, with an efficiency of ~25%. Although some (but not all) of our results would be expected from neurons that use a firing-rate code to transmit information, the evidence suggests that visual neurons are well equipped to decode stimulus-related information on the basis of relative spike timing and ISI duration.