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Beaubois R, Cheslet J, Duenki T, De Venuto G, Carè M, Khoyratee F, Chiappalone M, Branchereau P, Ikeuchi Y, Levi T. BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network. Nat Commun 2024; 15:5142. [PMID: 38902236 PMCID: PMC11190274 DOI: 10.1038/s41467-024-48905-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative approaches such as electroceutics. Recent research in bioelectronics and neuromorphic engineering have fostered the development of the new generation of neuroprostheses for brain repair. However, achieving their full potential necessitates a deeper understanding of biohybrid interaction. In this study, we present a novel real-time, biomimetic, cost-effective and user-friendly neural network capable of real-time emulation for biohybrid experiments. Our system facilitates the investigation and replication of biophysically detailed neural network dynamics while prioritizing cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and a variety of biological cells as well as real-time emulation of diverse network configurations. We envision our system as a crucial step towards the development of neuromorphic-based neuroprostheses for bioelectrical therapeutics, enabling seamless communication with biological networks on a comparable timescale. Its embedded real-time functionality enhances practicality and accessibility, amplifying its potential for real-world applications in biohybrid experiments.
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Affiliation(s)
- Romain Beaubois
- IMS, CNRS UMR5218, Bordeaux INP, University of Bordeaux, Talence, France
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan
| | - Jérémy Cheslet
- IMS, CNRS UMR5218, Bordeaux INP, University of Bordeaux, Talence, France
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan
| | - Tomoya Duenki
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
| | | | - Marta Carè
- DIBRIS, University of Genova, Genova, Italy
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Farad Khoyratee
- IMS, CNRS UMR5218, Bordeaux INP, University of Bordeaux, Talence, France
| | - Michela Chiappalone
- DIBRIS, University of Genova, Genova, Italy
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | | | - Yoshiho Ikeuchi
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
| | - Timothée Levi
- IMS, CNRS UMR5218, Bordeaux INP, University of Bordeaux, Talence, France.
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Shinji Y, Okuno H, Hirata Y. Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control. Front Neurosci 2024; 18:1220908. [PMID: 38726031 PMCID: PMC11079192 DOI: 10.3389/fnins.2024.1220908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and synaptic plasticity mechanisms may allow us to analyze cerebellar information processing in the real world by applying it to adaptive control of actual machines. Several artificial cerebellums have previously been constructed, but they require high-performance hardware to run in real-time for real-world machine control. Presently, we implemented an artificial cerebellum with the size of 104 spiking neuron models on a field-programmable gate array (FPGA) which is compact, lightweight, portable, and low-power-consumption. In the implementation three novel techniques are employed: (1) 16-bit fixed-point operation and randomized rounding, (2) fully connected spike information transmission, and (3) alternative memory that uses pseudo-random number generators. We demonstrate that the FPGA artificial cerebellum runs in real-time, and its component neuron models behave as those in the corresponding artificial cerebellum configured on a personal computer in Python. We applied the FPGA artificial cerebellum to the adaptive control of a machine in the real world and demonstrated that the artificial cerebellum is capable of adaptively reducing control error after sudden load changes. This is the first implementation and demonstration of a spiking artificial cerebellum on an FPGA applicable to real-world adaptive control. The FPGA artificial cerebellum may provide neuroscientific insights into cerebellar information processing in adaptive motor control and may be applied to various neuro-devices to augment and extend human motor control capabilities.
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Affiliation(s)
- Yusuke Shinji
- Department of Computer Science, Graduate School of Engineering, Chubu University, Kasugai, Japan
| | - Hirotsugu Okuno
- Faculty of Information Science and Technology, Osaka Institute of Technology, Hirakata, Japan
| | - Yutaka Hirata
- Department of Artificial Intelligence and Robotics, College of Engineering, Chubu University, Kasugai, Japan
- Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Japan
- Academy of Emerging Sciences, Chubu University, Kasugai, Japan
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Kumar G, Zhou Z, Wang Z, Kwan KM, Tin C, Ma CHE. Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia. CNS Neurosci Ther 2024; 30:e14638. [PMID: 38488445 PMCID: PMC10941591 DOI: 10.1111/cns.14638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 01/09/2024] [Accepted: 02/01/2024] [Indexed: 03/18/2024] Open
Abstract
AIMS The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits. METHODS We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system. RESULTS Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS. CONCLUSION Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.
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Affiliation(s)
- Gajendra Kumar
- Department of NeuroscienceCity University of Hong KongHong KongHong Kong SAR
| | - Zhanhong Zhou
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Zhihua Wang
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Kin Ming Kwan
- School of Life Sciences, Center for Cell and Developmental Biology and State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongHong KongHong Kong SAR
| | - Chung Tin
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Chi Him Eddie Ma
- Department of NeuroscienceCity University of Hong KongHong KongHong Kong SAR
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Yang S, Wang J, Zhang N, Deng B, Pang Y, Azghadi MR. CerebelluMorphic: Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:4398-4412. [PMID: 33621181 DOI: 10.1109/tnnls.2021.3057070] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.
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Solouki S, Mehrabi F, Mirzaii-Dizgah I. Localization of long-term synaptic plasticity defects in cerebellar circuits using optokinetic reflex learning profile. J Neural Eng 2022; 19. [PMID: 35675762 DOI: 10.1088/1741-2552/ac76df] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/08/2022] [Indexed: 11/12/2022]
Abstract
Objective.Functional maps of the central nervous system attribute the coordination and control of many body movements directly or indirectly to the cerebellum. Despite this general picture, there is little information on the function of cerebellar neural components at the circuit level. The presence of multiple synaptic junctions and the synergistic action of different types of plasticity make it virtually difficult to determine the distinct contribution of cerebellar neural processes to behavioral manifestations. In this study, investigating the effect of long-term synaptic changes on cerebellar motor learning, we intend to provide quantitative criteria for localizing defects in the major forms of synaptic plasticity in the cerebellum.Approach.To this end, we develop a firing rate model of the cerebellar circuits to simulate learning of optokinetic reflex (OKR), one of the most well-known cerebellar-dependent motor tasks. In the following, by comparing the simulated OKR learning profile for normal and pathosynaptic conditions, we extract the learning features affected by long-term plasticity disorders. Next, conducting simulation with different massed (continuous with no rest) and spaced (interleaved with rest periods) learning paradigms, we estimate the detrimental impact of plasticity defects at corticonuclear synapses on short- and long-term motor memory.Main results.Our computational approach predicts a correlation between location and grade of the defect with some learning factors such as the rate of formation and retention of motor memory, baseline performance, and even cerebellar motor reserve capacity. Further, spacing analysis reveal the dependence of learning paradigm efficiency on the spatiotemporal characteristic of defect in the network. Indeed, defects in cortical memory formation and nuclear memory consolidation mainly harm massed and spaced learning, respectively. This result is used to design a differential assay for identifying the faulty phases of cerebellar learning.Significance.The proposed computational framework can help develop neural-screening systems and prepare meso-scale functional maps of the cerebellar circuits.
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Affiliation(s)
- Saeed Solouki
- Department of Neurology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Farzad Mehrabi
- Department of Neurology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Iraj Mirzaii-Dizgah
- Department of Physiology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran
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Gandolfi D, Puglisi FM, Boiani GM, Pagnoni G, Friston KJ, D'Angelo EU, Mapelli J. Emergence of associative learning in a neuromorphic inference network. J Neural Eng 2022; 19. [PMID: 35508120 DOI: 10.1088/1741-2552/ac6ca7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
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Affiliation(s)
- Daniela Gandolfi
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Francesco Maria Puglisi
- DIEF, Universita degli Studi di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena, MO, 41121, ITALY
| | - Giulia Maria Boiani
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Giuseppe Pagnoni
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Karl J Friston
- Institute of Neurology, University College London, 23 Queen Square, LONDON, WC1N 3BG, London, WC1N 3AR, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Egidio Ugo D'Angelo
- Department Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Pavia, Lombardia, 27100, ITALY
| | - Jonathan Mapelli
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, 41125, ITALY
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Kuriyama R, Casellato C, D'Angelo E, Yamazaki T. Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units. Front Cell Neurosci 2021; 15:623552. [PMID: 33897369 PMCID: PMC8058369 DOI: 10.3389/fncel.2021.623552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent role in reproducing and predicting the dynamics of the microcircuits. To reconstruct a microcircuit, one must choose neuron and synapse models, placements, connectivity, and numerical simulation methods according to anatomical and physiological constraints. For reconstruction and refinement, it is useful to be able to replace one module easily while leaving the others as they are. One way to achieve this is via a scaffolding approach, in which a simulation code is built on independent modules for placements, connections, and network simulations. Owing to the modularity of functions, this approach enables researchers to improve the performance of the entire simulation by simply replacing a problematic module with an improved one. Casali et al. (2019) developed a spiking network model of the cerebellar microcircuit using this approach, and while it reproduces electrophysiological properties of cerebellar neurons, it takes too much computational time. Here, we followed this scaffolding approach and replaced the simulation module with an accelerated version on graphics processing units (GPUs). Our cerebellar scaffold model ran roughly 100 times faster than the original version. In fact, our model is able to run faster than real time, with good weak and strong scaling properties. To demonstrate an application of real-time simulation, we implemented synaptic plasticity mechanisms at parallel fiber-Purkinje cell synapses, and carried out simulation of behavioral experiments known as gain adaptation of optokinetic response. We confirmed that the computer simulation reproduced experimental findings while being completed in real time. Actually, a computer simulation for 2 s of the biological time completed within 750 ms. These results suggest that the scaffolding approach is a promising concept for gradual development and refactoring of simulation codes for large-scale elaborate microcircuits. Moreover, a real-time version of the cerebellar scaffold model, which is enabled by parallel computing technology owing to GPUs, may be useful for large-scale simulations and engineering applications that require real-time signal processing and motor control.
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Affiliation(s)
- Rin Kuriyama
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Claudia Casellato
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Tadashi Yamazaki
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
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Luo J, Firflionis D, Turnbull M, Xu W, Walsh D, Escobedo-Cousin E, Soltan A, Ramezani R, Liu Y, Bailey R, ONeill A, Idil AS, Donaldson N, Constandinou T, Jackson A, Degenaar P. The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics. IEEE Trans Biomed Eng 2020; 67:3004-3015. [PMID: 32091984 DOI: 10.1109/tbme.2020.2973934] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.
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Buccelli S, Bornat Y, Colombi I, Ambroise M, Martines L, Pasquale V, Bisio M, Tessadori J, Nowak P, Grassia F, Averna A, Tedesco M, Bonifazi P, Difato F, Massobrio P, Levi T, Chiappalone M. A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks. iScience 2019; 19:402-414. [PMID: 31421595 PMCID: PMC6706626 DOI: 10.1016/j.isci.2019.07.046] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/18/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022] Open
Abstract
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.
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Affiliation(s)
- Stefano Buccelli
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Yannick Bornat
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Ilaria Colombi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Matthieu Ambroise
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Laura Martines
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Valentina Pasquale
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Marta Bisio
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neurosciences, University of Padova, Via Nicolò Giustiniani 5, 35128 Padova, Italy
| | - Jacopo Tessadori
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Przemysław Nowak
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy; Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, 90-924 Lodz, Poland
| | - Filippo Grassia
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; University of Picardie Jules Verne, Laboratory of Innovative Technologies (LTI, EA 3899), Avenue des Facultés, Le Bailly, 80025 Amiens, France
| | - Alberto Averna
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Mariateresa Tedesco
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Paolo Bonifazi
- School of Physics and Astronomy, Tel Aviv University, 69978 Tel Aviv, Israel; Computational Neuroimaging Laboratory, Biocruces Health Research Institute, Hospital Universitario Cruces, Baracaldo, Vizcaya 48903, Spain; Ikerbasque: The Basque Foundation for Science, Bilbao, Bizkaia 48013, Spain
| | - Francesco Difato
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Timothée Levi
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; LIMMS CNRS-IIS, The University of Tokyo, 153-8505 Tokyo, Japan.
| | - Michela Chiappalone
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.
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