Advanced neuronal logic circuit designs using spiking models: a framework for sequential biocomputation

Abstract With conventional silicon-based computing approaching practical limits, biocomputing is being explored as a promising complement. Neuronal biocomputing is investigated for potential gains in energy efficiency, on-chip learning, and integration with biological systems. We explore logic gates and sequential circuits in spiking neuronal models that mimic motifs from conventional computer architectures. We propose a design framework that combines biophysically inspired spiking models, optimisation, and simulation for neuronal logic circuits. We demonstrate, in silico, NAND gates, SR latches, and D flip-flops implemented with spiking neurons. We configure synaptic conductances, introduce neuronal buffers for synchronisation, and specify network topologies. We encode binary information in spiking patterns and mitigate synchronisation issues using neuronal buffers and inhibitory control. Our results indicate effective and scalable neuronal logic circuits and, showing that they maintain a stable metabolic burden even in complex data storage configurations. Overall, this work provides a reproducible basis for logic and storage in spiking networks and lays the groundwork for future biological and neuromorphic implementations.