Using Neuronal Networks to Mimic Boolean Logic Gates

Classical computers use diodes and transistors to act as electronic switches, allowing the processing and storage of information. These switches are building blocks for logic circuits, which provide the basis for computing. After significant years of research, advancements in computing architecture have led to more efficient systems. However, classical computing architectures struggle with parallel processing, where a system will do multiple tasks simultaneously. The human brain, in contrast, processes information through a network of approximately 86 billion neurons, accounting for environmental perception, decision creation, and motor control. Modeling electronic circuits using neurons poses fundamental issues with splitting signals and timing. This project proposes two systems to help facilitate the switch from classical hardware circuits that use transistors to neuronal circuits. By developing the Yield Neuron Circuit which allows neuronal circuit signals to propagate simultaneously and the Clone Neuron Circuit duplicates the state of a neuron, the study presents an effective way to duplicate neuron states and match neuron signal propagation timings. The study presents a model that computes arithmetic, comparisons, and sends flags while continuing the utilization of registers throughout the CPU bus. In conclusion, the goal of this study is to propose a neuronal circuit model that is capable of complex functions similar to that of an Arithmetic Logic Unit.