Unifying and generalizing models of neural dynamics during decision-making

01/13/2020
by   David M. Zoltowski, et al.
16

An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a unifying framework for modeling neural activity during decision-making tasks. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state-space models, for which we introduce a scalable variational Laplace-EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the trial-averaged responses of a set of parietal neurons than a single accumulator model. Next, we identified a variable lower boundary in the responses of an LIP neuron during a random dot motion task.

READ FULL TEXT

page 1

page 3

page 4

page 8

page 9

page 12

page 13

page 14

research
08/04/2022

Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals

Human decision making behavior is observed with choice-response time dat...
research
05/11/2019

Towards a Quantum-Like Cognitive Architecture for Decision-Making

We propose an alternative and unifying framework for decision-making tha...
research
03/03/2023

Spectral learning of Bernoulli linear dynamical systems models for decision-making

Latent linear dynamical systems with Bernoulli observations provide a po...
research
09/27/2017

Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations

A fundamental problem in neuroscience is to characterize the dynamics of...
research
12/31/2021

Inferring perceptual decision making parameters from behavior in production and reproduction tasks

Bayesian models of behavior have provided computational level explanatio...
research
03/08/2021

Statistical Neuroscience in the Single Trial Limit

Individual neurons often produce highly variable responses over nominall...
research
06/20/2023

Computing a human-like reaction time metric from stable recurrent vision models

The meteoric rise in the adoption of deep neural networks as computation...

Please sign up or login with your details

Forgot password? Click here to reset