Neurons in Polymer: Hardware Neural Units Based on Polymer Memristive Devices and Polymer Transistors

We present here incremental steps toward realizing a tangible polymer neuromorphic architecture in the form of McCulloch-Pitts (nonspiking) neurons made from polymer electronics components, namely, memristive read-only-memory devices, transistors, and resistors. In the implementation, the polymer memristive devices perform the equivalent of synaptic weighting, while a polymer resistor subcircuit performs the equivalent of somatic summing. The sum is sent to a single transistor to apply the activation function. The complete circuit approximates the function of a single neural unit, which would form the basis for a hardware artificial neural network. It is shown here that a single, two-input unit, fit with three memristive devices per input, can perform continuous value classification applied to an active tether application, with a maximum error of 5%.