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Real-time unsupervised learning of visual stimuli in Neuromorphic VLSI systems

by Federico Corradi — last modified Sep 02, 2013 03:06 PM

Introduction

 

We demonstrate learning in a neuromophic recurrent attractor network distributed onto two VLSI chips. On a monitor we present some stimuli which are input to the network through the neuromorphic retina. Stimulation induces modification in the synaptic weights up to the point in which the selective reverberant states of activity are supported in the absence of stimulus. The network activity and the evolution of the synaptic matrix are monitored during learning.  Attractor memories exhibit interesting properties of error correction and they are robust to intervening distractors.

 

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6418494

 

Biocas 2012 Demo

 

Federico Corradi and Massimiliano Giulioni

http://www.biocas2012.org/p_demons.html

 

 

biocas slide learning1

 

biocas slide learning2

 

 

IEEE Swiss CAS/ED Student Workshop 2012

 

http://www.ieee.ch/chapters/cas-ed/cas-ed-news/2012-09-14/

 

unsupervised learning vlsi