Time Encoding Machines

Aurel Lazar
Columbia University

Monday, November 5, 2007 at 1:00PM

54-134 Engineering IV Building
Refreshments Served

Abstract: Time encoding is a real-time asynchronous mechanism for mapping amplitude information into a time sequence. Time Encoding Machines (TEMs) encode analog information in the time domain using only asynchronous circuits. Representation in the time domain is an alternative to the classical sampling representation in the amplitude domain. Applications arise in low power nano-sensors for analog-to-discrete (A/D) conversion as well as in modeling olfactory systems, vision and hearing in neuroscience. We investigate whether a bandlimited signal encoded with a single-input single-output (SISO) TEM can be recovered from the time sequence. In order to do so, we provide a signal recovery scheme and derive conditions for perfect recovery. We demonstrate that our results apply to signals encoded with TEMs realized with Asynchronous Sigma/Delta modulators, FM modulators as well as integrate-and-fire neurons. We generalize these results to single-input multi-output TEMs implemented using SISO TEMs. Finally, we extend the latter results to TEMs built with Hodgkin-Huxley neurons.

Biography: Aurel A. Lazar is a Professor of Electrical Engineering at Columbia University. In the mid 1980s and 1990s, he pioneered investigations into networking games and programmable networks. In addition, he conducted research in broadband networking with quality of service constraints; and in architectures, network management and control of telecommunications networks. His current research interests are at the intersection of Computational Neuroscience, Information/Communications Theory and Systems Biology. In silico, his focus is on Time Encoding and Information Representation in Sensory Systems, and, Spike Processing and Neural Computation in the Cortex. In vivo, his focus is on the olfactory system of the Drosophila.