Search Results for author:
Found 4 papers, 1 papers with code
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings
Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis.
A Flexible Bayesian Clustering of Dynamic Subpopulations in Neural Spiking Activity
To identify populations directly related to neural activity, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (mixDPFA) model, with the number of clusters and dimension of latent factors for each cluster treated as unknown parameters.
Dynamic modeling of spike count data with Conway-Maxwell Poisson variability
The CMP distribution can flexibly describe firing patterns that are both under- and over-dispersed relative to the Poisson distribution.
Tracking fast and slow changes in synaptic weights from simultaneously observed pre- and postsynaptic spiking
Using simulations, we first show that this model can accurately recover time-varying synaptic weights 1) for both depressing and facilitating synapses, 2) with a variety of long-term changes (including realistic changes, such as due to STDP), 3) with a range of pre- and post-synaptic firing rates, and 4) for both excitatory and inhibitory synapses.
