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

07/06/2015
by   Neil C. Rabinowitz, et al.
0

Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signals can confound or bias the inferences that one derives from spiking responses. Recent work demonstrates that for sensory neurons, these effects can be captured by a modulated Poisson model, whose rate is the product of a stimulus-driven response function and an unknown modulatory signal. Here, we extend this model, by incorporating explicit modulatory elements that are known (specifically, spike-history dependence, as in previous models), and by constraining the remaining latent modulatory signals to be smooth in time. We develop inference procedures for fitting the entire model, including hyperparameters, via evidence optimization, and apply these to simulated data, and to responses of ferret auditory midbrain and cortical neurons to complex sounds. We show that integrating out the latent modulators yields better (or more readily-interpretable) receptive field estimates than a standard Poisson model. Conversely, integrating out the stimulus dependence yields estimates of the slowly-varying latent modulators.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/01/2022

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

In many areas of the brain, neural spiking activity covaries with featur...
research
02/06/2017

Deep Learning Models of the Retinal Response to Natural Scenes

A central challenge in neuroscience is to understand neural computations...
research
02/18/2022

Preferential Sampling for Bivariate Spatial Data

Preferential sampling provides a formal modeling specification to captur...
research
01/26/2022

A probabilistic latent variable model for detecting structure in binary data

We introduce a novel, probabilistic binary latent variable model to dete...
research
02/11/2022

Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration

Network architectures and learning principles are key in forming complex...
research
01/27/2023

Statistical whitening of neural populations with gain-modulating interneurons

Statistical whitening transformations play a fundamental role in many co...

Please sign up or login with your details

Forgot password? Click here to reset