ANALOG CMOS NEURAL CIRCUITS — IN SITU LEARNING

This paper presents a tutorial review of artificial neurons and synapses which are implemented as analog CMOS circuits and which employ in situ learning of the synaptic weights. In situ learning implies that circuits local to the synapses perform computation of the weight updates according to built-in learning rules. This makes these synapses less flexible than those with external learning which do not commit to predetermined rules. On the other hand, in situ learning has potential advantages in increased learning rates in compensating for component inaccuracies and in adapting to nonstationary tasks. Both supervised and unsupervised learning rules and their implementations are described. The discussion focusses on synapses whose weights are stored on capacitors or in binary registers but also includes EEPROMs and CCDs. The emphasis is on synapses implemented using analog multipliers but other circuit techniques such as pulse streams are also briefly mentioned.