Title :
An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses
Author :
Badon, Davide ; Giulioni, Massimiliano ; Dante, Vittorio ; Del Giudice, Paolo
Author_Institution :
Univ. Tor Vergata, Roma
Abstract :
We illustrate key features of an analog, VLSI (aVLSI) chip implementing a network composed of 32 integrate-and-fire (IF) neurons with firing rate adaptation (AHP current), endowed with both a recurrent synaptic connectivity and AER-based connectivity with external, AER-compliant devices. Synaptic connectivity can be reconfigured at will as for the presence/absence of each synaptic contact and the excitatory/inhibitory nature of each synapse. Excitatory synapses are plastic through a spike-driven stochastic, Hebbian mechanism, and possess a self-limiting mechanism aiming at an optimal use of synaptic resources for Hebbian learning
Keywords :
Hebbian learning; VLSI; analogue integrated circuits; reconfigurable architectures; recurrent neural nets; AER-based connectivity; Hebbian learning; aVLSI recurrent network; analog VLSI chip; excitatory synapse; firing rate adaptation; inhibitory synapse; integrate-and-fire neurons; plastic synapse; reconfigurable synapse; recurrent synaptic connectivity; self-limiting mechanism; spike-driven stochastic Hebbian mechanism; spiking neuron network; Biological system modeling; Circuit noise; Hebbian theory; Neuromorphics; Neurons; Noise generators; Physics; Plastics; Stochastic processes; Very large scale integration;
Conference_Titel :
Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on
Conference_Location :
Island of Kos
Print_ISBN :
0-7803-9389-9
DOI :
10.1109/ISCAS.2006.1692813
