Understanding the biological-electrical transduction mechanisms is essential for reliable neural signal recording and feature extraction. As an alternative to state-of-the-art lumpedelement circuit models, here we adopt a multiscale-multiphysics finite-element modeling framework. The model couples ion transport with the Hodgkin-Huxley model and the readout circuit, and is used to investigate a few relevant case studies. This approach is amenable to explore ion transport in the extracellular medium otherwise invisible to circuit model analysis.

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

A design-oriented numerical study of vertical Si-nanowires to be used as sensing elements for the detection of the intracellular electrical activity of neurons. An equivalent lumped-element circuit model is derived and validated by comparison with physics-based numerical simulations. Most of the component values can be identified individually by geometrical and physical considerations. The transfer function and the SNR of the sensor in presence of thermal noise are derived, and the impact of the device geometry is shown.