Multiple neural spike train data analysis: state-of-the-art and future challenges

  title={Multiple neural spike train data analysis: state-of-the-art and future challenges},
  author={Emery N. Brown and Robert E. Kass and Partha P. Mitra},
  journal={Nature Neuroscience},
Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss… 

Neuronal Spike Train Analysis Using Gaussian Process Models

Gaussian process models for estimating time-varying firing rates of neurons are focused on and it is shown how this approach can be extended for modeling synchrony among multiple neurons.

Neural encoding based on frequency states using multi-spike train data

  • Fanxing HuHui Wei
  • Computer Science, Biology
    2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)
  • 2010
The results by analyzing multi-microelectrodes simultaneous recorded spike train from temporal cortex and hippocampus of mouse experiment is shown, supporting and validating the encoding method based on neuron firing frequency states and its transformation model under certain stimulus.

Spike Train kernels for multiple neuron recordings

  • Taro Tezuka
  • Computer Science, Biology
    2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
The mixture kernel was found to be most effective in analyzing multineuron spike trains, and the optimum parameter obtained from training this kernel was close to a biologically plausible value, suggesting that this approach is effective for seeking an appropriate model for the activity of a set of neurons.

Multi-channel neural data analysis methods and applications.

The methods for multi-channel neural data analysis are reviewed and a brief introduction of their typical application for studying different kinds of neural data is given.

Common-input models for multiple neural spike-train data

A multivariate point-process model in which the observed activity of a network of neurons depends on three terms: the experimentally-controlled stimulus; the spiking history of the observed neurons; and a hidden term that corresponds, for example, to common input from an unobserved population of neurons that is presynaptic to two or more cells in the observed population.

Swift Two-sample Test on High-dimensional Neural Spiking Data.

This work develops an approach to pretreat neural data to become independent samples over time by transferring the correlation of dynamics for each neuron in different sampling time bins into therelation of dynamics among different dimensions within each sampling time bin.

Reconstruction of neural network topology using spike train data: Small-world features of hippocampal network

  • Qi SheW. SoRosa H. M. Chan
  • Computer Science
    2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2015
An efficient frame-work for reconstructing the functional connectivity from the spike train data curated from the CRCNS program using a modified generalized linear model (GLM) framework with L1 norm penalty to investigate 10 datasets.

An Overview of Bayesian Methods for Neural Spike Train Analysis

  • Z. Chen
  • Computer Science, Biology
    Comput. Intell. Neurosci.
  • 2013
A tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels is presented.

A New Method for Multiple Spike Train Analysis Based on Information Discrepancy

A new measurement of information discrepancy, which is based on the comparisons of subsequence distributions, is applied to deal with a group of spike trains and analyze the synchronization pattern among the neurons, where the analytical result mostly depends on the experimental data and is affected little by subjective interference.



Information and Statistical Structure in Spike Trains

Careful data analysis is an essential complement to theoretical modeling that allows validation of theoretical model predictions and provides biologically relevant constraints and parameter values for further analytic and simulation studies.

Non-parametric significance estimation of joint-spike events by shuffling and resampling

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This work derives a reversible-jump Markov chain Monte Carlo (MCMC) algorithm and illustrates its application using simulated three-dimensional data and real four-dimensional feature vectors extracted from tetrode recordings of rat entorhinal cortex neurons.

Nerve Cell Spike Train Data Analysis: A Progression of Technique

The article reviews a progression of statistical analysis techniques: description, association as measured by moments and correlation, regression, and finally likelihood.

Statistical Significance of Coincident Spikes: Count-Based Versus Rate-Based Statistics

This work reformulated the statistical test underlying unitary-event analysis, using a coincidence count distribution based on empirical spike counts rather than on estimated spike probabilities, and demonstrates that the test power can be increased by a factor of two or more in physiologically realistic regimes.

Dynamic Analyses of Information Encoding in Neural Ensembles

A general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal is presented and an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted is suggested.

A review of methods for spike sorting: the detection and classification of neural action potentials.

This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands.

Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

This work presents test statistics for detecting the presence of higher-order interactions in spike train data by parameterizing these interactions in terms of coefficients of log-linear models and presents a Bayesian approach for inferring the existence or absence of interactions and estimating their strength.

Decoding Spike Trains Instant by Instant Using Order Statistics and the Mixture-of-Poissons Model

It is demonstrated that data from neurons in primary visual cortex are well fit by a mixture of Poisson processes; in this special case, the computations are substantially faster.

Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data

A method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA) is described, which accounts for these potentially interesting nonstationarities and allows locating them in time.