24 citations · 30 across the 4 of their papers we have counts for
5 papers
Online computation of sparse representations of time varying stimuli using a biologically motivated neural network
Tao Hu, Dmitri B. Chklovskii
Natural stimuli are highly redundant, possessing significant spatial and temporal correlations. While sparse coding has been proposed as an efficient strategy employed by neural sy…
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
Tao Hu, Dmitri B. Chklovskii
One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially…
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…
Super-resolution using Sparse Representations over Learned Dictionaries: Reconstruction of Brain Structure using Electron Microscopy
Tao Hu, Juan Nunez-Iglesias, Shiv Vitaladevuni +6
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging…
Sparse LMS via Online Linearized Bregman Iteration
Tao Hu, Dmitri B. Chklovskii
We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizi…