• Corpus ID: 18232804

Learning Exact Patterns of Quasi-synchronization among Spiking Neurons from Data on Multi-unit Recordings

  title={Learning Exact Patterns of Quasi-synchronization among Spiking Neurons from Data on Multi-unit Recordings},
  author={Laura Martignon and Kathryn B. Laskey and Gustavo Deco and Eilon Vaadia},
This paper develops arguments for a family of temporal log-linear models to represent spatio-temporal correlations among the spiking events in a group of neurons. The models can represent not just pairwise correlations but also correlations of higher order. Methods are discussed for inferring the existence or absence of correlations and estimating their strength. A frequentist and a Bayesian approach to correlation detection are compared. The frequentist method is based on G2 statistic with… 
Using Helmholtz Machines to Analyze Multi-channel Neuronal Recordings
This work presents an algorithm for automated discovery of stochastic firing patterns in large ensembles of neurons, from the "Helmholtz Machine" family, which attempts to predict the observed spike patterns in the data.
Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance
A novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons, designed to deal with nonstationary firing rates is described.
Nonparametric Data Selection for Improvement of Parametric Neural Learning: A Cumulant-Surrogate Method
A nonparametric cumulant based statistical approach for detecting linear and nonlinear statistical dependences in non-stationary time series and measuring the predictability which tests the null hypothesis of statistical independence by the surrogate method is introduced.


Bayesian Learning of Loglinear Models for Neural Connectivity
A Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies applies a Markov Chain Monte Carlo Model Composition (MC3) algorithm to search over connectivity structures and uses Laplace's method to approximate posterior probabilities of structures.
Spatiotemporal firing patterns in the frontal cortex of behaving monkeys.
Spatiotemporal firing patterns revealed by a method that detects all excessively repeating patterns regardless of their complexity or single-unit composition suggest that the patterns were generated by reverberations in a synfire mode within self-exciting cell assemblies.
Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
Abstract The Bayes factor is a ratio of two posterior normalizing constants, which may be difficult to compute. We compare several methods of estimating Bayes factors when it is possible to simulate
Accurate Approximations for Posterior Moments and Marginal Densities
These approximations to the posterior means and variances of positive functions of a real or vector-valued parameter, and to the marginal posterior densities of arbitrary parameters can also be used to compute approximate predictive densities.
Corticonics: Neural Circuits of Cerebral Cortex
This work has shown that not only is the probability for synaptic contact between neurons in the cortex high, but also the relationship between membrane potential and synaptic response curve is low.
The organization of behavior.
  • E. Capaldi
  • Education, Medicine
    Journal of applied behavior analysis
  • 1992
Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Discrete Multivariate Analysis.
[Neuronal circuits of the cerebral cortex].
  • J. Szentágothai
  • Biology
    Bulletin de l'Academie royale de medecine de Belgique
  • 1970
l991)Corticonics: Neural Circuits of the Cerebral Cortex
  • l991)Corticonics: Neural Circuits of the Cerebral Cortex
  • 1991