@InProceedings{Zoltowski2020icml,
  author    = {Zoltowski, David M and Pillow, Jonathan W and
  Linderman, Scott W},
  title     = {A general recurrent state space framework for modeling neural dynamics during decision-making},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year      = {2020},
  editor    = {Hal Daumé III and Aarti Singh},
  volume    = {119},
  series    = {Proceedings of Machine Learning Research},
  pages     = {11680--11691},
  address   = {Virtual},
  month     = {13--18 Jul},
  publisher = {PMLR},
  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 general framework for modeling neural activity during decision-making. 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 responses of a set of parietal neurons than a single accumulator model, and we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task. We expect this framework will be useful for modeling neural dynamics in a variety of decision-making settings.},
  pdf       = {http://proceedings.mlr.press/v119/zoltowski20a/zoltowski20a.pdf},
  url       = {http://proceedings.mlr.press/v119/zoltowski20a.html},
}