SIPLab
Sensory Information Processing Lab

software


Simulator for dynamic neural tissue under DIC microscopy

Code to implement the simulation framework described in the manuscript "Cell Membrane Tracking in Living Brain Tissue using Differential Interference Contrast Microscopy" by Lee, Kolb, Forest and Rozell. Code by J. Lee.

Dynamic filtering for sparse signals

Code to implement the algorithm described in the paper "Dynamic filtering of time-varying sparse signals via L1 minimization" by Charles, Balavoine and Rozell. Code by A. Charles.

Tracking sparse signals via L1 filtering

Code to produce the results in the paper "Discrete and Continuous-time Soft-Thresholding with Dynamic Inputs" by Balavoine, Rozell and Romberg. Code by A. Balavoine.

Inhibitory interneuron models via convex optimization

Code to produce the results in the paper "Modeling biologically realistic inhibitory interneurons in sensory coding models" in PLoS Computational Biology (2015) by Zhu and Rozell. Code by M. Zhu.

Spatial Filtering for Sparse Signals

Code to implement the algorithm described in the paper "Spectral super-resolution of hyperspectral imagery using re-weighted L1 spatial filtering" in IEEE GRSL (2014) by Charles and Rozell. Code by A. Charles.

Convergence speed of a dynamical system performing sparse coding

Code to produce the results in the paper "Convergence Speed of a Dynamical System for Sparse Recovery" in IEEE Trans. Signal Processing (2013) by Balavoine, Rozell and Romberg. Code by A. Balavoine.

Sparse Coding Dictionary Learning Library

Code to implement the sparse coding dictionary learning algorithm described in "Emergence of simple-cell receptive field properties by learning a sparse code for natural images" in Nature (1996) by Olshausen and Field. This codebase is a modern implementation of this algorithm, taking advantage of multicore architectures and efficient numerical methods for L1 minimization. Code by A. Charles.