Optimal solid state neurons

Kamal Abu-Hassan,Paul G. Morris,Julian F. R. Paton,Giacomo Indiveri,Zuner A. Bortolotto,Elisa Donati,Alain Nogaret,Joseph D. Taylor

doi:10.1038/s41467-019-13177-3

Abstract

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.

Highlights

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons
We demonstrate the high fidelity of the analytical model to the electronics by observing nearly identical membrane voltage oscillations in response to the same current injection protocols
The SSN model is first validated by its ability to predict the membrane voltage oscillations of the SSN hardware implemented in verylarge-scale integration (VLSI) (Fig. 1) when both are configured with the same parameters and stimulated with the same current protocol

Summary

Introduction

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. We demonstrate the high fidelity of the analytical model to the electronics by observing nearly identical membrane voltage oscillations in response to the same current injection protocols. This gave a high degree of confidence that bias parameters extracted from model optimization could be automatically dialled in the electronic device to predict biological behaviour. We discuss the dynamics of gate variables and ionic currents predicted by the SSN model These results demonstrate the possibility of making bionic chips that can reproduce the response of biological cells in terms of electrical activity

Methods

Results

Conclusion