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Found 13 papers, 6 papers with code
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.
Inferring Inference
Given a time series of neural activity during a perceptual inference task, our framework finds (i) the neural representation of relevant latent variables, (ii) interactions between these variables that define the brain's internal model of the world, and (iii) message-functions specifying the inference algorithm.
SIXO: Smoothing Inference with Twisted Objectives
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions.
Streaming Inference for Infinite Non-Stationary Clustering
Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents.
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
We allow the nature of these interactions to change over time by using a discrete set of dynamical states.
Bayesian recurrent state space model for rs-fMRI
We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI).
Mutually Regressive Point Processes
Despite many potential applications, existing point process models are limited in their ability to capture complex patterns of interaction.
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
However, current sensors and imaging approaches still face significant limitations in SNR and sampling frequency; therefore statistical denoising and interpolation methods remain critical for understanding single-trial spatiotemporal dendritic voltage dynamics.
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.
Point process latent variable models of larval zebrafish behavior
However, these sequences of swim bouts belie a set of discrete and continuous internal states, latent variables that are not captured by standard point process models.
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data.
Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models
In order to fully understand the neural activity of Caenorhabditis elegans, we need a rich, quantitative description of the behavioral outputs it gives rise to.
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions.
