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.
Neurons are the nervous system s primary computing elements.
Its stored memory is contained in the pattern and strength of the analog synapses that connect it to other neurons. Like neural tissue, arrays of silicon synapse transistors simultaneously and in parallel perform an analog computation and update their nonvolatile analog weight values. My goal, therefore, is to consider the computational principles on which neural systems operate, and to model and understand those principles in the silicon medium. Neural Networks , vol. Electron Devices, vol.