Abstract:
Traditionally analog signals are encoded in the amplitude domain. The reduction of the signal amplitude due to the drastically lower power supply in nanoscale semiconductors results in a very significant reduction in the accuracy of the analog circuits. A paradigm shift is in order. Instead of encoding the analog signal in the amplitude domain, time domain encoding should be used. Encoding signal information in the time domain leverages the trends in nanoscale semiconductors towards higher operational speeds and thus higher precision for time measurements.
The interest in temporal encoding in neuroscience is closely linked with the natural representation of sensory stimuli (signals) as a sequence of action potentials (spikes). Spikes are discrete time events that carry information about stimuli. Time encoding lies therefore at the interface between information/communication theory and asynchronous signal processing, on the one hand, and theoretical/computational neuroscience on the other. We shall demonstrate how questions of information representation and neural encoding can be successfully addressed with methods and intuitive arguments in all these fields.
More formally, Time Encoding Machines (TEMs) are asynchronous signal processors that encode analog information in the time domain. Asynchronous Sigma/Delta modulators as well as neural circuits based on integrate-and-fire (IAF) neurons and more general Hodgkin-Huxley neurons with feedback are instances of TEMs.
We show that for bandlimited signals (stimuli) with a known bandwidth, a perfect stimulus recovery from the train of spikes is possible provided that the spike density is above the Nyquist rate. These results are based on the key insight that neural encoding with a population of IAF neurons is akin to taking a set of measurements on the stimulus. These measurements or encodings can be represented as projections (inner products) of the stimulus on a set of sampling functions. We further show how to extend these findings to signals encoded with neural circuits with random thresholds and feedback.
For visual stimuli encoded with a population of spiking neurons, a number of intuitive operations on the original visual stimulus are demonstrated such as translations, rotations and zooming. All these operations are executed in the spike domain. We show that these operations can easily be realized with the same basic stimulus decoding algorithm. What changes in the recovery architecture, however, is the switching matrix (i.e., the input/output "wiring") of the spike domain switching building block. For example, for a particular setting of the switching matrix, the original stimulus is faithfully recovered. For other settings, translations, rotations and dilations (or combinations of these operations) of the original video stream are obtained.
Biography:
Aurel A. Lazar is a Professor of Electrical Engineering at Columbia University. In the mid 80s and 90s (www.ee.columbia.edu/~aurel/networking.html), 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 (www.bionet.ee.columbia.edu) 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.
LOCATION:151 Everitt UID:20091123T22282039@ece.uiuc.edu DTSTAMP:20091123T222820 CONTACT:Jont Allen and Todd Coleman CATEGORY:ECE 500 END:VEVENT END:VCALENDAR