Optimization of Biomolecular Neuristor Action Potentials to Mimic Biological Response

Biological neurons generate action potentials for communication and various computational and control tasks. Along with firing frequency, the shape and nonlinear dynamics of action potentials carry essential information and significant computational power. Many spiking neuron models take an integrate-and-fire approach to represent spike timing and frequency with computational simplicity but lack the biological relevance of more complex action potential dynamics. This paper introduces a model-based approach for optimizing the parameters of a soft neuristor circuit to match the action potential dynamics of Hodgkin-Huxley neurons. Genetic algorithms are used to optimize the neuristor parameters, with the objective of matching the neuristor’s response to biological action-potential-like references while respecting constraints on parameters. The approach is demonstrated through three numerical examples. The first seeks to match a reference action potential with a single neuristor, including both response shape and frequency. The second example optimizes the synaptic weights for a central pattern generator circuit. The final example uses the tuned neuristor model to steer a simulated car. This tool enables the creation and tuning of biologically-inspired controllers for engineered systems.