Now showing items 1-5 of 5
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 ...
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 ...
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 ...
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, ...
An analysis of training and generalization errors in shallow and deep networks
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2019-05-30)
This paper is motivated by an open problem around deep networks, namely, the apparent absence of overfitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze ...