DocumentCode :
827863
Title :
A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity
Author :
Indiveri, Giacomo ; Chicca, Elisabetta ; Douglas, Rodney
Author_Institution :
Swiss Fed. Inst. of Technol., the the Univ. of Zurich, Switzerland
Volume :
17
Issue :
1
fYear :
2006
Firstpage :
211
Lastpage :
221
Abstract :
We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron\´s response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.
Keywords :
VLSI; array signal processing; neural chips; protocols; VLSI array; address-event representation; asynchronous communication protocol; asynchronous event based communication infrastructure; bistable synapses; low-power spiking neurons; neuromorphic circuits; spike timing dependent plasticity; Adaptive arrays; Analog circuits; Asynchronous communication; Circuit simulation; Circuit topology; Network topology; Neurons; Protocols; Silicon; Very large scale integration; Address–event representation (AER); analog VLSI; integrate-and-fire (I&F) neurons; neuromorphic circuits; spike-based learning; spike-timing dependent plasticity (STDP); Algorithms; Microcomputers; Models, Neurological; Neural Networks (Computer); Neuronal Plasticity; Neurons; Synapses;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2005.860850
Filename :
1593704
Link To Document :
بازگشت