Graphical abstract

Brain-machine interfaces (BMI) were born to control “actions from thoughts” in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI—a neuromorphic chip for brain repair—to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary “bottom-up” approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of “finite size networks” which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments.

Summary Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction.

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48Recent advances in neurotechnology allow neurological impairments to be treated or 49 reduced by brain machine interfaces and neuroprostheses. To develop energy-efficient and 50 3 real-time capable devices, neuromorphic computing systems are envisaged as the core of 51 next-generation 'neurobiohybrid' systems for brain repair. We demonstrate here the first 52 exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two 53 neuronal populations, even when one is damaged or completely missing. We used in vitro 54 modular cell cultures to mimic the mutual interaction between neuronal assemblies and 55 created a focal lesion to functionally disconnect the two populations. Then, we employed 56 our neuromorphic prosthesis for two specific applications with future clinical 57 implications: bidirectional bridging to artificially reconnect two disconnected neuronal 58 modules and hybrid bidirectional bridging to replace the activity of one module with a 59 neuromorphic spiking neural network. Our neuroprosthetic system opens up new avenues 60 for the development of novel bioelectrical therapeutics for human applications. 61 62 65 the greatest impact carried by stroke (1) and traumatic brain injury (2), brain disorders are among 66 the leading causes of disabilities worldwide. Due to recent advances in neural and neuromorphic 67 engineering, direct interfacing of artificial circuits with large neuronal networks is possible to 68 develop novel 'neurobiohybrid' systems (such as neuroprostheses (3)), which are envisaged as 69 potentially interesting clinical applications for brain lesions (4). In this paper, we introduce an 70 innovative neuroprosthetic system based on a neuromorphic real-time interface that can re-71 establish the communication between two disconnected neuronal assemblies. 72 Neural interfaces are promising solutions for brain repair (5). Modern neural interfaces are mainly 73 designed to restore lost motor functions in only one direction, i.e., from the brain to the body (6) 74 or from the body to the brain (7). Additionally, recent neuroprosthetic developments have shown 75 4 the enormous potential of neural interfaces to aid and accelerate functional recovery (8, 9). 76 However, a major obstacle in developing novel neuroprosthetic devices for bidirectional 77 communication with and within the brain is the complex nature of interactions among different 78 brain areas, which in turn presents a challenge for the development of appropriate stimulation 79 protocols as well as for testing such devices using in vivo models (10). 80 Despite very recent technological progress (11, 12), in vivo models still have two main 81 bottlenecks. The first bottleneck is the technical challenge to faithfully reproduce specific/focal 82 network lesions (mainly due to their complexity) that the neuroprosthesis aims to treat, whereas 83 the second is the difficulty in disentangling the actual effect of the adopted electrical therapy from 84 the complex activity of a brain constantly pr...

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