This provides a framework for modeling neural dynamics with 1D accumulation to bound dynamics (e.g. the DDM), multi-dimensional accumulators, variable & collapsing boundaries, trial-history effects, and more!pic.twitter.com/YzwDxIUZZP
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This provides a framework for modeling neural dynamics with 1D accumulation to bound dynamics (e.g. the DDM), multi-dimensional accumulators, variable & collapsing boundaries, trial-history effects, and more!pic.twitter.com/YzwDxIUZZP
We demonstrated the approach on two sets of spiking recordings from monkey parietal cortex during decision-making, where we compared 1D vs. 2D accumulators and fit a ramping model with a variable lower bound.
To fit the models, we developed a scalable inference method for (r)SLDS models called vLEM. vLEM is a generalization of Laplace EM (see @jakhmack, 2011) for single-state LDS models.
I'll be presenting this at Cosyne 2020, so if you're at the conference and interested you can come by my poster to hear more!
Finally, code is available at https://github.com/davidzoltowski/ssmdm … for the models and at https://github.com/slinderman/ssm for vLEM
Cc @_marcustriplett (probably seen this already but seems up your street!)
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