A probabilistic latent variable model for detecting structure in binary data

01/26/2022
by   Christopher Warner, et al.
13

We introduce a novel, probabilistic binary latent variable model to detect noisy or approximate repeats of patterns in sparse binary data. The model is based on the "Noisy-OR model" (Heckerman, 1990), used previously for disease and topic modelling. The model's capability is demonstrated by extracting structure in recordings from retinal neurons, but it can be widely applied to discover and model latent structure in noisy binary data. In the context of spiking neural data, the task is to "explain" spikes of individual neurons in terms of groups of neurons, "Cell Assemblies" (CAs), that often fire together, due to mutual interactions or other causes. The model infers sparse activity in a set of binary latent variables, each describing the activity of a cell assembly. When the latent variable of a cell assembly is active, it reduces the probabilities of neurons belonging to this assembly to be inactive. The conditional probability kernels of the latent components are learned from the data in an expectation maximization scheme, involving inference of latent states and parameter adjustments to the model. We thoroughly validate the model on synthesized spike trains constructed to statistically resemble recorded retinal responses to white noise stimulus and natural movie stimulus in data. We also apply our model to spiking responses recorded in retinal ganglion cells (RGCs) during stimulation with a movie and discuss the found structure.

READ FULL TEXT

page 9

page 11

page 14

page 15

page 17

page 18

page 20

page 22

05/26/2022

Mesoscopic modeling of hidden spiking neurons

Can we use spiking neural networks (SNN) as generative models of multi-n...
01/11/2017

Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines

The retina is a complex nervous system which encodes visual stimuli befo...
06/23/2020

Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks

Hawkes process provides an effective statistical framework for analyzing...
07/04/2016

Cell assemblies at multiple time scales with arbitrary lag constellations

Hebb's idea of a cell assembly as the fundamental unit of neural informa...
07/06/2015

A model of sensory neural responses in the presence of unknown modulatory inputs

Neural responses are highly variable, and some portion of this variabili...
02/06/2017

Deep Learning Models of the Retinal Response to Natural Scenes

A central challenge in neuroscience is to understand neural computations...