A geographically distributed bio-hybrid neural network with memristive plasticity

Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired and bio-hybrid architectures are just beginning to materialise. The use of memristive technologies in brain-inspired architectures for computing or for sensing spiking activity of biological neurons8 are only recent examples, however interlinking brain and electronic neurons through plasticity-driven synaptic elements has remained so far in the realm of the imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where memristors work as "synaptors" between rat neural circuits and VLSI neurons. The two fundamental synaptors, from artificial-to-biological (ABsyn) and from biological-to- artificial (BAsyn), are interconnected over the Internet. The bNN extends across Europe, collapsing spatial boundaries existing in natural brain networks and laying the foundations of a new geographically distributed and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology

A low power architecture for AER event-processing microcontroller

This paper presents a custom MSP430™-compatible microcontroller, specifically tailored for quasi-digital processing Address Event Representation (AER) events. Main target applications are fully reprogrammable sensory systems where events pre-processing has to be carried out by means of easily-tunable elaboration algorithms; a microcontroller-based design could provide the right trade-off between flexibility and performance. Key features are good time resolution, high reactivity, on-demand only processing and power consumption reduction. The proposed architecture has been analyzed and compared with an open source MSP430TM-compliant microcontroller (openMSP430) in terms of performance and power consumption. Accurate and wide cases-spectrum simulations (targeting ASIC technology) show an average power consumption reduction ranging from 50 % (same operating frequency) up to 79 % (same maximum event rate); equivalently, with the same power budget, an average improvement of either resolution of 84 % or maximum event rate of 1020 % is obtained

Event-based softcore processor in a biohybrid setup applied to structural plasticity

The goal in neuromorphic engineering is to design circuits and systems which emulate the computational principles of biological nervous systems. As these circuits follow the same fundamental design principles as their biological counterparts, they represent an elegant solution for the design of bio-hybrid computing architectures. We present a neuromorphic bio-hybrid system in which electronic circuits are coupled directly to neuronal cell cultures for providing low-level access to biological signal processing. To form this bio-hybrid, we introduce a backbone system which allows the implements of different network topologies by routing Address-Event Representations (AER) of spikes from biological cells to neuromorphic circuits and vice versa. The use of a soft- embedded-processor realtime system allows the exploration of topology-evolving setups. The final goal in the application of this backbone system is the creation of a bio-hybrid neural network that is structurally plastic. Preliminary results have already been obtained in a first verification step, successfully forming a bio-hybrid neural network and thus promising a novel approach towards bioinspired Brain-Machine Interfaces