Signal Processing for Neural Spike Trains

European Physical Journal B, 2001

Probabilistic properties of spiking time-series obtained in vivo from singular neurons belonging to Red Nucleus of brain are analyzed for two groups of rats: genetically defined rat model of depression (Flinders Sensitive Rat Line - FSL) and a control (healthy) group. The FSL group shows a distribution of interspike intervals with a much longer tail than that found for normal rats. The former distribution (for the FSL group) indicates a power-law with exponent α = - 1±0.1. A simple thermodynamic (noise) model is elaborated to explain obtained results.

Neurocomputing, 2001

In a typical neurophysiological recording, the shape of the voltage trace due to an action potential depends, among other things, on the mutual geometry of the tissue and the electrode, and on the overall health of the preparation. These parameters change throughout the duration of the experiment, typically continuously and slowly, with occasional abrupt jumps. Here we propose a method to characterize such slow drifts by the estimation of a one-dimensional curved manifold, embedded in the large-dimensional space of spike shapes, along which the data cluster. We model each event as the closest point on this manifold, which is the maximum-likelihood expectation of its spike shape, and a residual, which can be analyzed subsequently. We apply the method to both intra-and extracellular recordings. 2001 Published by Elsevier Science B.V.

Trends in Neurosciences, 2007

Biochemical and Biophysical Research Communications, 2006

Neurons in the central nervous system are thought to program neural language via firing sequential spikes for guiding animal behaviors. The quantitative profiles of spike intrinsic properties are critically important to understand spike programming. We developed approaches with whole-cell recordings to measure the threshold potentials and refractory periods (RPs) of sequential spikes, and to analyze the relationships of these factors with spike timing precision and capacity at the regular-spiking and fast-spiking neurons in cortical slice. The RPs and threshold potentials of sequential spikes at these two groups of neurons are different and are linearly correlated with spike timing precision and capacity. These data suggest that RPs and threshold potentials essentially navigate the spike programming for the precise and loyal encoding of meaningful neural signals. Our study provides the avenues for decoding the spectrum of the neural signals quantitatively.

Analysis of Parallel Spike Trains, 2010

Understanding the nature and origin of neural variability at the level of single neurons and neural networks is fundamental to our understanding of how neural systems can reliably process information. This chapter provides a starting point to the empirical analysis and interpretation of the variability of single neuron spike trains. In the first part, we cover a number of practical issues of measuring the inter-spike interval variability with the coefficient of variation (CV) and the trialby-trial count variability with the Fano factor (FF), including the estimation bias for finite observations, the measurement from rate-modulated spike trains, and the timeresolved analysis of variability dynamics. In the second part, we specifically explore the effect of serial interval correlation in nonrenewal spike trains and the impact of slow fluctuations of neural activity on the relation of interval and count variability in stochastic models and in in vivo recordings from cortical neurons. Finally, we discuss how we can interpret the empirical results with respect to potential neuronintrinsic and neuron-extrinsic sources of single neuron output variability. M.P. Nawrot ( )

Physical Review E, 2009

The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.

Neurocomputing, 2007

Stationary spiking of single neurons is often modelled by a renewal point process. Here, we tested the underlying model assumption that the inter-spike intervals are mutually,independent,by analyzing,stationary spike train recordings from,individual rat neocortical neurons,in vivo and,in vitro. All neurons exhibited moderate (in vivo) or weak,(in vitro) negative,first order serial correlation,of neighboring,intervals which,was found to be significant in most cases.

Journal of Physiology-Paris, 2010

In the present overview, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. Our goal is a better understanding of the extent to which computing and modeling with spiking neuron networks might be biologically plausible and computationally efficient.

Frontiers in Computational Neuroscience, 2008

The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.