Unifying and generalizing models of neural dynamics during decision-making
This work addresses a bottleneck in systems neuroscience for researchers studying neural decision-making by enabling scalable fitting of more complex models, though it is incremental as it builds on existing state-space modeling approaches.
The authors tackled the problem of fitting diverse decision-making models to neural data by proposing a unifying framework that includes the drift-diffusion model and extensions like multi-dimensional accumulators, which they applied to monkey parietal cortex data and found that a two-dimensional accumulator better captured neural responses than a single accumulator model.
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.