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Channel identification machines.

Aurel A Lazar1, Yevgeniy B Slutskiy

  • 1Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. aurel@ee.columbia.edu

Computational Intelligence and Neuroscience
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying communication channels coupled with asynchronous samplers. The technique accurately recovers filter projections, crucial for signal processing in neuroscience and communications.

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Area of Science:

  • Signal Processing
  • System Identification
  • Computational Neuroscience

Background:

  • Asynchronous samplers are prevalent in neuroscience and communications.
  • Accurate channel identification is vital for understanding complex systems.
  • Existing methods may struggle with the intricacies of asynchronous sampling.

Purpose of the Study:

  • To develop a formal methodology for identifying communication channels in cascade with asynchronous samplers.
  • To devise algorithms capable of recovering filter projections loss-free.
  • To extend these methods to noisy circuit environments.

Main Methods:

  • Modeling the channel as a multidimensional filter.
  • Utilizing models of asynchronous samplers (e.g., integrate-and-fire neurons, sigma/delta modulators).
  • Employing reproducing kernel Hilbert spaces (RKHS) with Dirichlet kernels for test signals.

Main Results:

  • Developed channel identification algorithms that recover filter projections loss-free.
  • Demonstrated convergence of filter projections to the impulse response under specific conditions.
  • Extended the methodology to handle noisy circuits.

Conclusions:

  • The proposed methodology offers a robust approach to channel identification in systems with asynchronous samplers.
  • The findings are applicable to various signal processing tasks in neuroscience and communications.
  • The method provides accurate filter recovery even in the presence of noise.