Now showing items 1-5 of 5
Brain Signals Localization by Alternating Projections
(Center for Brains, Minds and Machines (CBMM), arXiv, 2019-08-29)
We present a novel solution to the problem of localization of brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS) criterion by the alternating projection (AP) algorithm, ...
Hippocampal Remapping as Hidden State Inference
(Center for Brains, Minds and Machines (CBMM), bioRxiv, 2019-08-22)
Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact ...
Function approximation by deep networks
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2019-05-30)
We show that deep networks are better than shallow networks at approximating functions that can be expressed as a composition of functions described by a directed acyclic graph, because the deep networks can be designed ...
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
(Center for Brains, Minds and Machines (CBMM), 2019-08-17)
While deep learning is successful in a number of applications, it is not yet well understood theoretically. A satisfactory theoretical characterization of deep learning however, is beginning to emerge. It covers the following ...
Technical Report: Building a Neural Ensemble Decoder by Extracting Features Shared Across Multiple Populations
To understand whether and how a certain population of neurons represent behavioral-relevant vari- ables, building a neural ensemble decoder has been used to extract information from the recorded activity. Among different ...