hierarchical inference task
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
We study how recurrent neural networks (RNNs) solve a hierarchical inference task involving two latent variables and disparate timescales separated by 1-2 orders of magnitude. The task is of interest to the International Brain Laboratory, a global collaboration of experimental and theoretical neuroscientists studying how the mammalian brain generates behavior.
Review for NeurIPS paper: Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
Additional Feedback: POST REBUTTAL EDIT: I read all reviews and the author response. Clarity and lack of details were a major issue - and it seems that the authors will amend this. Most of my concerns in this regard were lack of details, and therefore I am satisfied with the proposed edits. Statistics - the authors state that they checked four networks but do not plan to do any statistics. I think statistics would greatly strengthen the paper, and urge the authors to do them for the final version.
Review for NeurIPS paper: Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
This is a solid paper that definitely warrants acceptance. The paper is clearly written and makes multiple substantive contributions in terms of training and analyzing RNNs at the boundary of ML and neuroscience. The reviewers identified some issues, primarily relating to clarity and requests for additional details. There is reasonable confidence that updates by the authors will satisfy these requests. There is an emerging field of research involving neural networks being trained to solve tasks that are used in neuroscience experiments to allow comparisons between the representations and dynamics learned by artificial systems and those observed in real neural recordings.
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
We study how recurrent neural networks (RNNs) solve a hierarchical inference task involving two latent variables and disparate timescales separated by 1-2 orders of magnitude. The task is of interest to the International Brain Laboratory, a global collaboration of experimental and theoretical neuroscientists studying how the mammalian brain generates behavior. First, RNNs learn behavior that is quantitatively similar to ideal Bayesian baselines. Second, RNNs perform inference by learning a two-dimensional subspace defining beliefs about the latent variables. Third, the geometry of RNN dynamics reflects an induced coupling between the two separate inference processes necessary to solve the task.