Channel Identification Machines (CIMs) Toolbox

The Channel Identification Machines (CIMs) Toolbox provides a set of algorithms for the functional identification of a channel in a system consisting of a communication channel in cascade with an asynchronous sampler, as well as step-by-step demonstrations of the accompanying source code. The channel considered here is modeled as a multidimensional filter, while models of asynchronous sampler are taken from neuroscience and communications and includes integrate-and-fire (IAF) neurons, asynchronous delta-sigma modulators (ASDM), and general oscillators in cascade with zero-crossing detectors.

The CIM algorithms identify the dendritic processing filter and reconstruct its kernel with arbitrary precision.


MATLAB Release

CIM Toolbox 0.03 [code] (July 20, 2013)

Python Release

CIM Toolbox 0.01 [code ] (January 15, 2013)


The latest source code and documentation written by Yevgeniy B. Slutskiy and Chung-Heng Yeh are available on the Bionet GitHub repository.

  1. Identifying Dendritic Processing ,

    Aurel A. Lazar and Yevgeniy B. Slutskiy , Advances in Neural Information Processing Systems 23 , J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta (eds.) , pp. 1261-1269 , 2010

  2. Channel Identification Machines ,

    Aurel A. Lazar and Yevgeniy B. Slutskiy , Journal of Computational Intelligence and Neuroscience , Volume 2012

  3. Functional Identification of Spike-Processing Neural Circuits ,

    Aurel A. Lazar and Yevgeniy B. Slutskiy , Neural Computation , Volume 26 , Number 2 , MIT Press , pp. 264-305 , February 2014