Testing the odds of inherent vs. observed overdispersion in neural spike counts.

  title={Testing the odds of inherent vs. observed overdispersion in neural spike counts.},
  author={Wahiba Taouali and Giacomo Benvenuti and Pascal Wallisch and Fr{\'e}d{\'e}ric Chavane and Laurent Udo Perrinet},
  journal={Journal of neurophysiology},
  volume={115 1},
The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous… 

Flexible models for spike count data with both over- and under- dispersion

It is found that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives.

Dynamic modeling of spike count data with Conway-Maxwell Poisson variability

A dynamic model with Conway-Maxwell Poisson (CMP) observations that provides a framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience.

Modelling the neural code in large populations of correlated neurons

A class of models of large-scale population activity based on the theory of exponential family distributions is proposed and applied to macaque primary visual cortex recordings, and it is shown they capture a wide range of response statistics, facilitate accurate Bayesian decoding, and provide interpretable representations of fundamental properties of the neural code.

A dynamic model for decoding direction and orientation in macaque primary visual cortex.

The temporal tuning model reveals that the decoding is highly dynamic and evolves with the global population activation state, at odds to static decoding commonly used for early visual areas and shows that direction can be decoded accurately from a poorly selective neuronal population.

A minority-ruled population coding of kinematics in the striatum

It is proposed that during motor learning, striatal ensembles adjust their task representation by tuning the activity of a minority of neurons to the kinematic parameters most relevant to motor performance.

Responses of neurons in macaque MT to unikinetic plaids.

The notion that pattern selective neurons in area MT integrate component motions that differ widely in speed, and that they do so in a way that is consistent with an intersection-of-constraints model, is supported.

Pediatric Anxiety Disorders: A Cost of Illness Analysis

Few studies provide information about the clinical correlates of economic costs in pediatric anxiety disorders. This study uses baseline data from a randomized trial involving 209 children and

Model Comparison in Approximate Bayesian Computation

Researchers at the Bernstein Center for Computational Neuroscience present a novel probabilistic procedure called “supervised injections” to correct for the “confusion” in the response of the immune system to shocks.

Comparing neural simulations by neural density estimation

This work proposes an efficient method to perform Bayesian model comparison for simulation-based models using a mixture-density network to map features of the observed data to the parameters of the posterior over models and presents an application to a use case scenario from computational neuroscience.



Testing the Odds of Inherent versus Observed Over-dispersion in Neural Spike Counts Odds of Inherent versus Observed Over-dispersion

This paper describes how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts and quantifies the odds that over-dispersion could be due to the limited number of repetitions (trials), and compares the performance of this model to the Poisson model on a population decoding task.

Spike Count Reliability and the Poisson Hypothesis

A new statistical technique is presented for assessing the significance of observed variability in the neural spike counts with respect to a minimal Poisson hypothesis, which avoids the conventional but troubling assumption that the spiking process is identically distributed across trials.

The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs

  • W. SoftkyC. Koch
  • Biology
    The Journal of neuroscience : the official journal of the Society for Neuroscience
  • 1993
It is argued that neurons that act as temporal integrators over many synaptic inputs must fire very regularly and only in the presence of either fast and strong dendritic nonlinearities or strong synchronization among individual synaptic events will the degree of predicted variability approach that of real cortical neurons.

Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex

Surprisingly, trial-to-trial variability of membrane potential is found to be low in neurons of visual cortex, and a simple deterministic mechanism amplifies the low variability of summated synaptic inputs into the large variability of firing rate.

Beyond Poisson: Increased Spike-Time Regularity across Primate Parietal Cortex

Responses of neurons in macaque MT to stochastic motion signals.

The responses of neurons in area MT of the alert monkey are measured while the strength and direction of the motion signal in such displays while the relationship between response magnitude and response variance conforms to a power law with an exponent slightly greater than 1.

Stimulus-dependent variability and noise correlations in cortical MT neurons

It is shown that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability.

Partitioning neuronal variability

A model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT.