10 citations · 19 across the 8 of their papers we have counts for
8 papers
A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation
Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin +4
We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window of its input history onto a ca…
The Neuron as a Direct Data-Driven Controller
Jason Moore, Alexander Genkin, Magnus Tournoy +3
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…
Neural optimal feedback control with local learning rules
Johannes Friedrich, Siavash Golkar, Shiva Farashahi +3
A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. A prominent framework for addressing s…
A Neural Network for Semi-Supervised Learning on Manifolds
Alexander Genkin, Anirvan M. Sengupta, Dmitri Chklovskii
Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural…
Distributed Coordinate Descent for Generalized Linear Models with Regularization
Ilya Trofimov, Alexander Genkin
Generalized linear model with and regularization is a widely used technique for solving classification, class probability estimation and regression problems. With the n…
Distributed Coordinate Descent for L1-regularized Logistic Regression
Ilya Trofimov, Alexander Genkin
Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memor…
A Neuron as a Signal Processing Device
Tao Hu, Zaid J. Towfic, Cengiz Pehlevan +2
A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understo…
A network of spiking neurons for computing sparse representations in an energy efficient way
Tao Hu, Alexander Genkin, Dmitri B. Chklovskii
Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy effi…