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Found 6 papers, 4 papers with code
The Neuron as a Direct Data-Driven Controller
In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective.
Neural optimal feedback control with local learning rules
This network performs system identification and Kalman filtering, without the need for multiple phases with distinct update rules or the knowledge of the noise covariances.
A Neural Network for Semi-Supervised Learning on Manifolds
Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i. e., they respond to a small neighborhood of stimulus space.
Distributed Coordinate Descent for Generalized Linear Models with Regularization
Generalized linear model with $L_1$ and $L_2$ regularization is a widely used technique for solving classification, class probability estimation and regression problems.
Distributed Coordinate Descent for L1-regularized Logistic Regression
Solving logistic regression with L1-regularization in distributed settings is an important problem.
