Computational-neuroscience research is increasingly in need of larger, biophysically realistic brain models. These analog-in-nature models build upon the Hodgkin-Huxley (HH) formalism and are run on digital, high-performance computing systems making simulation very computationall
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Computational-neuroscience research is increasingly in need of larger, biophysically realistic brain models. These analog-in-nature models build upon the Hodgkin-Huxley (HH) formalism and are run on digital, high-performance computing systems making simulation very computationally expensive. In circuit form, these models are theoretically suitable for efficient analog implementation. However, the ion-channel components –predominantly, sodium and potassium– are nonlinear, time-varying resistors, lacking an efficient implementation. Chua et al. proved that these ion-channel models are in fact memristors –devices with a conductance as a function of applied-voltage history– claiming that “memristors are the right stuff for building brains”. However, the kind of actual memristor implementation that is the right one for building brains is not defined. In this article, the device class and characteristics of such memristors are defined and existing memristive implementations of HH-like designs are then reviewed. Surprisingly, although often misclassified as such, no physical implementation currently exists that replicates the original HH equations faithfully or efficiently. Having put forward the desired memristor properties, a design guide for screening suitable memristor designs is then proposed. Screening the existing literature reveals that suitable devices likely already exist for potassium ion-channel emulation, while none exists for sodium; this calls for further investigation of higher-order, voltage-controlled and volatile memristors.
