DocumentCode :
3468175
Title :
A Solid State Neuron for the Realisation of Highly Scaleable Third Generation Neural Networks
Author :
Chen, Yajie ; Hall, Steve ; McDaid, Liam ; Buiu, Octavian ; Kelly, Peter
Author_Institution :
Dept. of Electr. Eng. & Electron., Liverpool Univ.
fYear :
2006
fDate :
23-26 Oct. 2006
Firstpage :
1071
Lastpage :
1073
Abstract :
The paper describes a novel silicon synapse based on a two stage charge-coupled device with the weighting functionality integrated into the first stage by means of a floating gate. A spike input to the second gate allows the charge under the first gate to drift onto the floating diffusion output stage to produce a voltage spike. Parallel defined synapses are assigned to the drain of the input MOSFET of the current mirror which serves as the point neuron. This configuration provides a summing function and allows the transfer of the spikes. The neuron and its associated solid state synapses comprise a highly compact and scaleable neural cell which operates at very low power levels because of its charge coupled operation
Keywords :
MOSFET circuits; charge-coupled device circuits; neural nets; current mirror; floating diffusion output; floating gate; input MOSFET; parallel synapses; scaleable neural cell; scaleable third generation neural networks; silicon synapse; solid state neuron; summing function; two stage charge-coupled device; weighting functionality; Biology computing; Capacitors; Character generation; Charge coupled devices; Computer networks; Neural networks; Neurons; Silicon; Solid state circuits; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Solid-State and Integrated Circuit Technology, 2006. ICSICT '06. 8th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
1-4244-0160-7
Electronic_ISBN :
1-4244-0161-5
Type :
conf
DOI :
10.1109/ICSICT.2006.306684
Filename :
4098325
Link To Document :
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