DocumentCode :
1099906
Title :
Synchrony detection and amplification by silicon neurons with STDP synapses
Author :
Bofill-i-Petit, Adria ; Murray, Alan F.
Author_Institution :
Sch. of Eng. & Electron., Univ. of Edinburgh, UK
Volume :
15
Issue :
5
fYear :
2004
Firstpage :
1296
Lastpage :
1304
Abstract :
Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.
Keywords :
Hebbian learning; VLSI; feedforward neural nets; neural chips; synchronisation; binary weight distribution; feedforward network; learning rule; neuromorphic analog very large scale integration; silicon neurons; spike timing dependent synaptic plasticity; spike timing synchrony; synchrony amplification; synchrony detection; Biological systems; Circuits; Delay; Hebbian theory; Neural network hardware; Neuromorphics; Neurons; Silicon; Timing; Very large scale integration; Action Potentials; Animals; Artificial Intelligence; Brain; Humans; Learning; Microcomputers; Models, Neurological; Nerve Net; Neural Networks (Computer); Neural Pathways; Neuronal Plasticity; Neurons; Synaptic Transmission; Time Factors;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2004.832842
Filename :
1333090
Link To Document :
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