Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Neural mechanisms for analog to digital conversion|
|Citation:||BioMEMS and nanotechnology : 10-12 December 2003, Perth, Australia / Dan V. Nicolau, Uwe R. Muller, John M. Dell (eds.), pp. 278-286|
|Series/Report no.:||Proceedings of SPIE--the International Society for Optical Engineering ; 5275.|
|Conference Name:||BioMEMS and Nanotechnology (1st : 2003 : Perth, Australia)|
|Mark D. McDonnell, Derek Abbott, and Charles E. Pearce|
|Abstract:||Consider an array of threshold devices, such as neurons orcomparators, where each device receives the same input signal, butis subject to independent additive noise. When the output fromeach device is summed to give an overall output, the system actsas a noisy Analog to Digital Converter (ADC). Recently, such asystem was analyzed in terms of information theory, and it wasshown that under certain conditions the transmitted informationthrough the array is maximized for non-zero noise. Such aphenomenon where noise can be of benefit in a nonlinear system istermed Stochastic Resonance (SR). The effect in the array ofthreshold devices was termed Suprathreshold Stochastic Resonance(SSR) to distinguish it from conventional forms of SR, in whichusually a signal needs to be subthreshold for the effect to occur.In this paper we investigate the efficiency of the analog todigital conversion when the system acts like an array of simple neurons, by analyzing the average distortion incurred and comparing this distortion to that of a conventional flash ADC.|
|Description:||© 2004 COPYRIGHT SPIE--The International Society for Optical Engineering.|
|Appears in Collections:||Applied Mathematics publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.