Here, we propose an analogue circuit modelling generic ion channels designed for this purpose. A biologically accurate neuromorphic design is therefore needed, whose mathematical model is compatible with nonlinear optimization, and that responds identically to a biological neuron under any current injection. In biology, this width varies from one type of neuron to another. For example, conventional silicon technology 4, 43 assigns a constant thermal voltage to the width of the transition region from the open to the closed state of an ionic gate. Transferring dynamic information from a biological cell to a biomimetic circuit is met with additional difficulties arising from hardware constraints 39, 40, 41, 42. Constrained nonlinear optimization has further allowed nonlinear parameters, such as voltage thresholds and recovery times to be inferred, which was key to predicting the dynamic state of a biological neuron 35, 36, 37, 38. Approaches ranging from hand-tuning 20 to trial-and-error fitting 21, 22, 23, 24, multi-objective functions 25, 26, 27, 28, genetic algorithms 29, Bayesian inference 30 and statistical interpolation 31, 32, 33, 34 have been implemented to estimate maximal ionic conductances. Recent efforts at building quantitative computational models of neurons have focussed on generalizing the HodgkinвЂ“Huxley (HH) model to multi-channel models 18, 19.

Analogue asynchronous electronics is the most promising way to integrate raw nervous stimuli 17 instantaneously, independently of system size and complexity. The increasing focus on implantable bioelectronics to treat chronic disease is however changing this paradigm and is instilling new urgency in the need for low-power analogue solid-state devices that accurately mimic biocircuits. Although silicon neurons 4, 5, 6, 7, 8, synapses 9 and brain inspired networks 10, 11, 12, 13, 14, 15, 16 have been proposed, these designs were not meant to reiterate the behaviour of biological cells in complete detail, but to search for the organizing principles of biology that can be applied to practical devices. The difficulty of measuring microscopic parameters that control the dynamics of ionic currents 2 and the nonlinearity of ionic conductances 3 has hampered so far theoretical efforts to build quantitative computational models and subsequently neuromorphic devices replicating the exact response of a biological neuron. The electrical properties of biological cells have long been studied to understand the intracellular dynamics underpinning membrane voltage oscillations 1. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. 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 inВ silico. Here we estimate the parameters of highly nonlinear conductance models and derive the abВ initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. However, designing such circuits remains a challenge. Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons.

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