Abstract
Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.
Highlights
The growing demand in miniature and energy-efficient electronic interface with bioelectrical activity for personalized medicine and other related products essentially depends on development of biohybrid electronic technologies (Vassanelli and Mahmud, 2016)
As compared to the previous works (Vassanelli and Mahmud, 2016; Chiolerio et al, 2017) focused on general trends and approaches for interfacing between neuronal and extrinsic/intrinsic neuromorphic systems, here we provide a comprehensive analysis of the implementation of a CMOS-integrated hybrid system based on scalable memristive devices and arrays back-end-of-line or monolithically integrated with CMOS circuits, analog signal processing on CMOS chips with memristive and microelectrode arrays
The concept of a single neurohybrid chip is proposed based on existing and future solutions in the field of neural cells and microfluidic technologies, which allow spatial structuring of living neural network combined with CMOS MEA and memristive arrays for real-time recording, processing, and stimulation of bioelectric activity interfaced and controlled by mixed analog–digital circuits on the same chip
Summary
The growing demand in miniature and energy-efficient electronic interface with bioelectrical activity for personalized medicine and other related products essentially depends on development of biohybrid electronic technologies (Vassanelli and Mahmud, 2016). Further development of memory-embedded sensors (Tzouvadaki et al, 2015; Doucey and Carrara, 2019) and neurohybrid systems, including neuroprostheses based on the integration of memristive and microelectrode CMOS technologies, as well as spiking neural network (SNN) architectures, will ensure the processing and real-time classification of electrophysiological and other analog signals, related to the activity of biological neuronal networks. The solution of such problems could be strongly optimized by exploiting a highly specialized processor with neural network architecture adapted for this specific kind of calculation and serving as if it is a natural extension of the biological nervous system (Boi et al, 2016) In this case, the computing device would be capable of processing a large input dimension (determined by the number of electrodes in the MEA) and performing the required real-time signal processing. We should not limit ourselves with the reimplementation of the part of the nervous system for patients, we could envision the further development of augmented nervous systems with digital extensions using memristive properties of self-adaptation for the bidirectional brain to machine interfaces (Musk and Neuralink, 2019) based on the proposed neurohybrid chip approach
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