We describe an approach to analyzing single- and multi-unit (ensemble) discharge patterns based on information-theoretic distance measures and on empirical theories derived from work in universal signal processing. In this approach, we quantify the difference between response patterns, be they time-varying or not, using information-theoretic distance measures. We apply these techniques to single and multiple unit processing of sound amplitude and sound location. These examples illustrate that neurons can simultaneously represent at least two kinds of information with different levels of fidelity. The fidelity can persist through a transient and a subsequent steady-state response, indicating that it is possible for an evolving neural code to represent information with constant fidelity. 1 Johnson et al. Analysis of Neural Coding 1 Introduction Neural coding has been classified into two broadly defined types: rate codes the average rate of spike discharge and timing codes the t...

We analyze the effect of noise in integrate-and-fire neurons driven by timedependent input, and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent sub-threshold input, diffusive noise can be replaced by escape noise with a hazard function that has a Gaussian dependence upon the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.

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This paper presents general methods for obtaining power spectra of a large class of signals and random fields driven by an underlying point processes, in particular spatial shot noises with random impulse response and arbitrary basic stationary point processes described by their Bartlett spectrum, and signals or fields sampled at random times or points, where again the sampling point process is quite general. The formulas obtained clearly show the interaction between the underlying counting process, the sampled process or the impulse response. We also obtain the Bartlett spectrum for the general linear Hawkes spatial branching point process with random fertility rate and general immigrant process described by its Bartlett spectrum. Finally we obtain the Cramr spectrum of general spatial birth and death processes.

The time histogram method is the most basic tool for capturing a timedependent rate of neuronal spikes. Generally in the neurophysiological literature, the bin size that critically determines the goodness of the fit of the time histogram to the underlying spike rate has been subjectively selected by individual researchers. Here, we propose a method for objectively selecting the bin size from the spike count statistics alone, so that the resulting bar or line graph time histogram best represents the unknown underlying spike rate. For a small number of spike sequences generated from a modestly fluctuating rate, the optimal bin size may diverge, indicating that any time histogram is likely to capture a spurious rate. Given a paucity of data, the method presented here can nevertheless suggest how many experimental trials should be added in order to obtain a meaningful time-dependent histogram with the required accuracy.

We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.

The joint peristimulus time histogram (JPSTH) and cross-correlogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper (Cai et al. 2004a) we showed how a Bootstrap evaluation of the peaks in the smoothed diagonals of the JPSTH may be used to establish the likely validity of apparent time-varying correlation. As noted by Brody (1999a,b) and Ben-Shaul et al. (2001), trial-to-trial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a method of estimating the time-dependent trial-to-trial variation in spike trains that may exceed the natural variation displayed by Poisson and non-Poisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firing-rate function that fluctuates over time. The method developed here uses principal components of the trial-to-trial variability in firing rate functions to obtain a small number of parameters (typically two or three) that characterize the deviation of each trial’s firing rate function from the across-trial average firing rate, represented by the

The joint peristimulus time histogram (JPSTH) provides a visual representation of the dynamics of correlated activity for a pair of neurons. There are many ways to adjust the JPSTH for the time-varying firing-rate modulation of each neuron, and then to define a suitable measure of time-varying correlated activity. Our approach is to introduce a statistical model for the time-varying joint spiking activity so that the joint firing rate can be estimated more efficiently. We have applied an adaptive smoothing method, which has been shown to be effective in capturing sudden changes in firing rate, to the ratio of joint firing probability to the probability of firing predicted by independence. A Bootstrap procedure, applicable to both Poisson and non-Poisson data, was used to define a statistical significance test of whether a large ratio could be due to chance alone. A numerical simulation showed that the Bootstrap-based significance test has very nearly the correct rejection probability, and can have markedly better power to detect departures from independence than does an approach based on testing contiguous bins in the JPSTH. In a companion paper (Cai et al. 2004b) we show how this formulation can accommodate latency and time-varying excitability effects, which can confound spike timing effects.

Cortical neurons in vivo had been regarded as Poisson spike generators that convey no information other than the rate of random firing. Recently, using a metric for analyzing local variation of interspike intervals, researchers have found that individual neurons express specific patterns in generating spikes, which may symbolically be termed regular, random, or bursty, rather invariantly in time. In order to study the dynamics of firing patterns in greater detail, we propose here a Bayesian method for estimating firing irregularity and the firing rate simultaneously for a given spike sequence, and we implement an algorithm that may render the empirical Bayesian estimation practicable for data comprising a large number of spikes. Application of this method to electrophysiological data revealed a subtle correlation between the degree of firing irregularity and the firing rate for individual neurons. Irregularity of firing did not deviate greatly around the low degree of dependence on the firing rate and remained practically unchanged for individual neurons in the cortical areas V1 and MT, whereas it fluctuated greatly in the lateral geniculate nucleus of the thalamus. This indicates the presence and absence of autocontrolling mechanisms for maintaining patterns of firing in the cortex and thalamus, respectively.

Tremor is one of the most disabling symptoms in patients with many movement disorders including Parkinson's disease (PD) and essential tremor (ET). Neural tremor, which is measured by microelectrodes placed near nerve cells or obtained from brain cells during stereotactic neurosurgery, is due to the fluctuation of the firing rate of neurons. The frequency of this neural tremor varies over time and is nonstationary. We describe a frequency tracking method using the extended Kalman filter (EKF) to estimate the instantaneous tremor frequency (ITF) of binary spike trains detected from microelectrode recordings (MER). The results demonstrate that the EKF can accurately track fluctuations in tremor frequency even though the noise in binary spike trains is not Gaussian.