This work details CMOS, bio-inspired, bio-compatible circuits which were used as synapses between an artificial neuron and a living neuron and between two living neurons.An intracellular signal from a living neuron was amplified, an integrate-and-fire neuron was used as a simple processing element to detect the spikes, and an artificial synapse was used to send outputs to another living neuron.The key structure is an electronic synapse which is based around a floating-gate pFET.The charge on the floating-gate is analogous to the synaptic weight and can be modified.This modification can be viewed as similar to long-term potentiation and long-term depression.The modification can either be programmed (supervised learning) or can adapt to the inputs (unsupervised learning).Since the technology to change the floating-gate weight has greatly improved, these weights can be set quickly and accurately.Intrinsic floating-gate learning rules were explored and the ability to change the synaptic weight was shown.
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Bio-inspired, bio-compatible, reconfigurable analog CMOS circuits