Unifying and generalizing models of neural dynamics during decision-making

• URL: http://arxiv.org/abs/2001.04571v1
• Date: Mon, 13 Jan 2020 23:57:28 GMT
• Title: Unifying and generalizing models of neural dynamics during decision-making
• Authors: David M. Zoltowski, Jonathan W. Pillow, and Scott W. Linderman
• Abstract summary: 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.
• Score: 27.46508483610472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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