A neuromorphic architecture from single transistor neurons with organic bistable devices for weights

Artificial Intelligence (AI) has made tremendous progress since it was first postulated in the 1950s. However, AI systems are primarily emulated on serial machine hardware that result in high power consumption, especially when compared to their biological counterparts. Recent interest in neuromorphic architectures aims to more directly emulate biological information processing to achieve substantially lower power consumption for appropriate information processing tasks. We propose a novel way of realizing a neuromorphic architecture, termed Synthetic Neural Network (SNN), that is modeled after conventional artificial neural networks and incorporates organic bistable devices as circuit elements that resemble the basic operation of a binary synapse. Via computer simulation we demonstrate how a single synthetic neuron, created with only a single transistor, a single-bistable-device-per-input, and two resistors, exhibits a behavior of an artificial neuron and approximates the sigmoidal activation function. We also show that, by increasing the number of bistable devices per input, a single neuron can be trained to behave like a Boolean logic AND or OR gate. To validate the efficacy of our design, we show two simulations where SNN is used as a pattern classifier of complicated, non-linear relationships based on real-world problems. In the first example, our SNN is shown to perform the trained task of directional propulsion due to water hammer effect with an average error of about 7.2%. The second task, a robotic wall following, resulted in SNN error of approximately 9.6%. Our simulations and analysis are based on the performance of organic electronic elements created in our laboratory.