The state variables in both electronic and nervous systems are analog. They are represented in electronic systems by electric charge, and in nervous systems by electric charge or by chemical concentrations. The mechanisms by which each systems manipulates its state variables to do computation, however, are vastly different. The nervous system retains the analog dynamic range, achieving noise immunity by adjusting the signal-detection threshold adaptively. Digital machines quantize their analog inputs, and use restoring logic at every computational step.
Nervous systems perform primarily analog computations, and quantize the computed result.
Unfortunately, we do not know what computational primitives neural systems use, how they represent information, or what their organizing principles are. However, because semiconductor electronics allows us to apply, at a high level of integration, a device physics similar to that used by neural tissue, I conclude that we should be able to build electronic circuits that mimic the computational primitives of nervous systems, and that we should be able to use these circuits to explore the organizational principles employed by neurobiology. The silicon retina and cochlea are now well developed and mimic some of the sensory preprocessing performed by living organisms. Our synapse transistors also compute the product of their stored analog memory and the applied input. Although we do not believe that a single device can model the complex behavior of a neural synapse completely, our synapse transistors do implement a local learning function.