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Found 7 papers, 3 papers with code
Efficient non-conjugate Gaussian process factor models for spike countdata using polynomial approximations
Gaussian Process Factor Analysis (GPFA) hasbeen broadly applied to the problem of identi-fying smooth, low-dimensional temporal struc-ture underlying large-scale neural recordings. However, spike trains are non-Gaussian, whichmotivates combining GPFA with discrete ob-servation models for binned spike count data. The drawback to this approach is that GPFApriors are not conjugate to count model like-lihoods, which makes inference challenging. Here we address this obstacle by introduc-ing a fast, approximate inference method fornon-conjugate GPFA models.
A general recurrent state space framework for modeling neural dynamics during decision-making
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision.
System Identification for Continuous-time Linear Dynamical Systems
This paper addresses system identification for the continuous-discrete filter, with the aim of generalizing learning for the Kalman filter by relying on a solution to a continuous-time It\^o stochastic differential equation (SDE) for the latent state and covariance dynamics.
Probing the relationship between linear dynamical systems and low-rank recurrent neural network models
We show that latent LDS models can only be converted to RNNs in specific limit cases, due to the non-Markovian property of latent LDS models.
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
Fast shared response model for fMRI data
The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli.
Bayesian Entropy Estimation for Countable Discrete Distributions
The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning.
Information Theory Information Theory
