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Optimal solid state neurons
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DOI：10.1038/s41467-019-13177-3.png)
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Abstract
En 中文
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.
Keywords:
CALCIUM CURRENT
ELECTROPHYSIOLOGICAL PROPERTIES
PARAMETER-ESTIMATION
SEARCH ALGORITHM
MODEL NEURONS
HYPERPOLARIZATION
DYNAMICS
NETWORK
ASSIMILATION
MODULATION
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