Frontiers | Tunable Neuromimetic Integrated System for Emulating Cortical Neuron Models

Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits.

In recent years, a new discipline called neuromorphic engineering has emerged which challenges classical approaches to engineering and computer research. There are two main aspects to neuromorphic engineering: neuromorphic modeling, which reproduces neuro-physiological phenomena in order to increase the understanding of the nervous systems, and neuromorphic computation, which uses the neuronal properties to build neurally inspired computing hardware. Neuromorphic engineering proposes to fill the gap between computational neurosciences, and low-power consumption engineering (Mead, 1989). As alternatives to software-based solutions (Brette et al., 2007; Davison et al., 2009) and parallel graphics processors (GPUs) to alleviate the significant computational cost (Wang et al., 2011), neuromorphic systems are often based on custom integrated circuits (IC; Indiveri et al., 2011) and systems (Misra and Saha, 2010). A neuromorphic system could be digital, analog, or mixed. Brüderle et al. (2011) describes a methodological framework for a fully automated translation between PyNN domain and appropriate hardware configurations. In our case, we chose an analog implementation of neuron models while the communication between neurons is digital. The main advantage of the analog implementation, compared to its numerical simulation, arises from the locally analog and parallel nature of the computations. Neuroscientists provide biological measurements to computational neuroscientists who then propose a model for simulation, and for studies of the single cell or neural network dynamics. The chip designer uses this model for the design of analog neuromimetic ICs (see Figure 1).

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