We describe and discuss an electronic implementation of an attractor n
eural network with plastic synapses. The network undergoes double dyna
mics, for the neurons as well as the synapses. Both dynamical processe
s are unsupervised. The synaptic dynamics is autonomous, in that it is
driven exlusively and perpetually by neural activities. The latter fo
llow the network activity via the developing synapses and the influenc
e of external stimuli. Such a network self-organizes and is a device w
hich converts the gross statistical characteristics of the stimulus in
put stream into a set of attractors (reverberations). To maintain for
long time the acquired memory, the analog synaptic efficacies are disc
retized by a stochastic refresh mechanism. The discretized synaptic me
mory has indefinitely long lifetime in the absence of activity in the
network. It is modified only by the arrival of new stimuli. The stocha
stic refresh mechanism produces transitions at low probability which e
nsures that transient stimuli do not create significant modifications
and that the system has large palimpsestic memory. A change in the att
ractor structure represents a major, macroscopic change in the statist
ics of the input stream, which may deform attractors, may create new o
nes and may eliminate others. The electronic implementation is complet
ely analogue, stochastic and asynchronous. The circuitry of the first
prototype is discussed in detail as well as the tests performed on it.
In carrying out the implementation we have been guided by biological
considerations and by electronic constraints. Both are discussed and n
ew insights and lessons for the learning process are proposed.