• Corpus ID: 248496331

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

  title={Dynamic modeling of spike count data with Conway-Maxwell Poisson variability},
  author={Ganchao Wei and Ian H. Stevenson},
In many areas of the brain, neural spiking activity covaries with fea-tures of the external world, such as sensory stimuli or an animal’s movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, here we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP… 

Figures from this paper



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.

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

This article describes how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts and quantifies the odds that overdispersion 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.

Empirical models of spiking in neural populations

This work argues that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling.

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

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.

Analysis of between-trial and within-trial neural spiking dynamics.

The results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and within-trial neural dynamics readily visible in raster plots, as well as the less apparent fast and intermediate timescale features of the neuron's biophysical properties.

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

Fully Bayesian inference for neural models with negative-binomial spiking

A powerful data-augmentation framework for fully Bayesian inference in neural models with negative-binomial spiking that substantially outperforms Poisson regression on held-out data, and reveals latent structure underlying spike count correlations in simultaneously recorded spike trains.

Local field potentials indicate network state and account for neuronal response variability

It is shown that neurons in primary visual cortex exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity.

Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering

This work uses the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains and suggests a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.