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Ades C, Abd MA, Hutchinson DT, Tognoli E, Du E, Wei J, Engeberg ED. Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration. Biomimetics (Basel) 2024; 9:78. [PMID: 38392124 PMCID: PMC10886511 DOI: 10.3390/biomimetics9020078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.
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Affiliation(s)
- Craig Ades
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Moaed A Abd
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | | | - Emmanuelle Tognoli
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - E Du
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Jianning Wei
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Erik D Engeberg
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
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2
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Reyes-Sanchez M, Amaducci R, Sanchez-Martin P, Elices I, Rodriguez FB, Varona P. Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons. Neural Netw 2023; 164:464-475. [PMID: 37196436 DOI: 10.1016/j.neunet.2023.04.034] [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: 12/28/2021] [Revised: 03/01/2023] [Accepted: 04/18/2023] [Indexed: 05/19/2023]
Abstract
Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal.
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Affiliation(s)
- Manuel Reyes-Sanchez
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
| | - Rodrigo Amaducci
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Pablo Sanchez-Martin
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Irene Elices
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain; Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | - Francisco B Rodriguez
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Pablo Varona
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
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3
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Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish. BIOPHYSICA 2023. [DOI: 10.3390/biophysica3010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Neuronal cultures are one of the most important experimental models in modern interdisciplinary neuroscience, allowing to investigate in a control environment the emergence of complex behavior from an ensemble of interconnected neurons. Here, I review the research that we have conducted at the neurophysics laboratory at the University of Barcelona over the last 15 years, describing first the neuronal cultures that we prepare and the associated tools to acquire and analyze data, to next delve into the different research projects in which we actively participated to progress in the understanding of open questions, extend neuroscience research on new paradigms, and advance the treatment of neurological disorders. I finish the review by discussing the drawbacks and limitations of neuronal cultures, particularly in the context of brain-like models and biomedicine.
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4
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Gandolfi D, Puglisi FM, Serb A, Giugliano M, Mapelli J. Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence. Front Cell Neurosci 2022; 16:1115395. [PMID: 36605614 PMCID: PMC9808067 DOI: 10.3389/fncel.2022.1115395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Francesco Maria Puglisi
- Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Alexander Serb
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh, United Kingdom
| | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy,*Correspondence: Jonathan Mapelli ✉
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5
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Ayasreh S, Jurado I, López-León CF, Montalà-Flaquer M, Soriano J. Dynamic and Functional Alterations of Neuronal Networks In Vitro upon Physical Damage: A Proof of Concept. MICROMACHINES 2022; 13:2259. [PMID: 36557557 PMCID: PMC9782595 DOI: 10.3390/mi13122259] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
There is a growing technological interest in combining biological neuronal networks with electronic ones, specifically for biological computation, human-machine interfacing and robotic implants. A major challenge for the development of these technologies is the resilience of the biological networks to physical damage, for instance, when used in harsh environments. To tackle this question, here, we investigated the dynamic and functional alterations of rodent cortical networks grown in vitro that were physically damaged, either by sequentially removing groups of neurons that were central for information flow or by applying an incision that cut the network in half. In both cases, we observed a remarkable capacity of the neuronal cultures to cope with damage, maintaining their activity and even reestablishing lost communication pathways. We also observed-particularly for the cultures cut in half-that a reservoir of healthy neurons surrounding the damaged region could boost resilience by providing stimulation and a communication bridge across disconnected areas. Our results show the remarkable capacity of neuronal cultures to sustain and recover from damage, and may be inspirational for the development of future hybrid biological-electronic systems.
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Affiliation(s)
- Sàlem Ayasreh
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Imanol Jurado
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Clara F. López-León
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Marc Montalà-Flaquer
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
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6
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Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, Barban F, Brofiga M, Averna A, Bonacini F, Guggenmos DJ, Bornat Y, Massobrio P, Bonifazi P, Levi T. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sci 2022; 12:1578. [PMID: 36421904 PMCID: PMC9688667 DOI: 10.3390/brainsci12111578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 08/27/2023] Open
Abstract
Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.
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Affiliation(s)
- Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Vinicius R. Cota
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marta Carè
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Mattia Di Florio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Romain Beaubois
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Federico Barban
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Francesco Bonacini
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - David J. Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
- Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Yannick Bornat
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
| | - Paolo Bonifazi
- IKERBASQUE, The Basque Fundation, 48009 Bilbao, Spain
- Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Timothée Levi
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
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7
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Abstract
The next robotics frontier will be led by biohybrids. Capable biohybrid robots require microfluidics to sustain, improve, and scale the architectural complexity of their core ingredient: biological tissues. Advances in microfluidics have already revolutionized disease modeling and drug development, and are positioned to impact regenerative medicine but have yet to apply to biohybrids. Fusing microfluidics with living materials will improve tissue perfusion and maturation, and enable precise patterning of sensing, processing, and control elements. This perspective suggests future developments in advanced biohybrids.
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8
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Chow SYA, Hu H, Osaki T, Levi T, Ikeuchi Y. Advances in construction and modeling of functional neural circuits in vitro. Neurochem Res 2022; 47:2529-2544. [PMID: 35943626 PMCID: PMC9463289 DOI: 10.1007/s11064-022-03682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 11/26/2022]
Abstract
Over the years, techniques have been developed to culture and assemble neurons, which brought us closer to creating neuronal circuits that functionally and structurally mimic parts of the brain. Starting with primary culture of neurons, preparations of neuronal culture have advanced substantially. Development of stem cell research and brain organoids has opened a new path for generating three-dimensional human neural circuits. Along with the progress in biology, engineering technologies advanced and paved the way for construction of neural circuit structures. In this article, we overview research progress and discuss perspective of in vitro neural circuits and their ability and potential to acquire functions. Construction of in vitro neural circuits with complex higher-order functions would be achieved by converging development in diverse major disciplines including neuroscience, stem cell biology, tissue engineering, electrical engineering and computer science.
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Affiliation(s)
- Siu Yu A Chow
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
| | - Huaruo Hu
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Tatsuya Osaki
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
| | - Timothée Levi
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
- IMS laboratory, CNRS UMR 5218, University of Bordeaux, Talence, France
| | - Yoshiho Ikeuchi
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan.
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9
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Di Florio M, Iyer V, Rajhans A, Buccelli S, Chiappalone M. Model-based online implementation of spike detection algorithms for neuroengineering applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:736-739. [PMID: 36086269 DOI: 10.1109/embc48229.2022.9871444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Traditional methods for the development of a neuroprosthesis to perform closed-loop stimulation can be complex and the necessary technical knowledge and experience often present a high barrier for adoption. This paper takes a novel Model-Based Design approach to simplifying such closed-loop system development, and thereby lowering the adoption barrier. This work implements a computational model of different spike detection algorithms in Simulink® and compares their performances by taking advantage of synthetic neural signals to evaluate suitability for the intended embedded implementation. Clinical Relevance--- Closed-loop systems have been demonstrated to be suitable for brain repair strategies. Coupling two different brain areas by means of a neuroprosthesis can potentially lead to restoration of communication by inducing activity-dependent plasticity.
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10
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Dias C, Castro D, Aroso M, Ventura J, Aguiar P. Memristor-Based Neuromodulation Device for Real-Time Monitoring and Adaptive Control of Neuronal Populations. ACS APPLIED ELECTRONIC MATERIALS 2022; 4:2380-2387. [PMID: 36571090 PMCID: PMC9778128 DOI: 10.1021/acsaelm.2c00198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neurons are specialized cells for information transmission and information processing. In fact, many neurologic disorders are directly linked not to cellular viability/homeostasis issues but rather to specific anomalies in electrical activity dynamics. Consequently, therapeutic strategies based on the direct modulation of neuronal electrical activity have been producing remarkable results, with successful examples ranging from cochlear implants to deep brain stimulation. Developments in these implantable devices are hindered, however, by important challenges such as power requirements, size factor, signal transduction, and adaptability/computational capabilities. Memristors, neuromorphic nanoscale electronic components able to emulate natural synapses, provide unique properties to address these constraints, and their use in neuroprosthetic devices is being actively explored. Here, we demonstrate, for the first time, the use of memristive devices in a clinically relevant setting where communication between two neuronal populations is conditioned to specific activity patterns in the source population. In our approach, the memristor device performs a pattern detection computation and acts as an artificial synapse capable of reversible short-term plasticity. Using in vitro hippocampal neuronal cultures, we show real-time adaptive control with a high degree of reproducibility using our monitor-compute-actuate paradigm. We envision very similar systems being used for the automatic detection and suppression of seizures in epileptic patients.
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Affiliation(s)
- Catarina Dias
- IFIMUP,
Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, Porto 4169-007, Portugal
| | - Domingos Castro
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
| | - Miguel Aroso
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
| | - João Ventura
- IFIMUP,
Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, Porto 4169-007, Portugal
| | - Paulo Aguiar
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
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11
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Petschenig H, Bisio M, Maschietto M, Leparulo A, Legenstein R, Vassanelli S. Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks. Front Neurosci 2022; 16:838054. [PMID: 35495034 PMCID: PMC9047904 DOI: 10.3389/fnins.2022.838054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data—multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state—their performance is similar during UP and DOWN states.
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Affiliation(s)
- Horst Petschenig
- Faculty of Computer Science and Biomedical Engineering, Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Marta Bisio
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Marta Maschietto
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Alessandro Leparulo
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Robert Legenstein
- Faculty of Computer Science and Biomedical Engineering, Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
- Robert Legenstein
| | - Stefano Vassanelli
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova, Padova, Italy
- *Correspondence: Stefano Vassanelli
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12
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Zeinolabedin SMA, Schuffny FM, George R, Kelber F, Bauer H, Scholze S, Hanzsche S, Stolba M, Dixius A, Ellguth G, Walter D, Hoppner S, Mayr C. A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:94-107. [PMID: 35025750 DOI: 10.1109/tbcas.2022.3142987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μW/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μW/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.
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13
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Hossaini A, Valeriani D, Nam CS, Ferrante R, Mahmud M. A Functional BCI Model by the P2731 working group: Physiology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | | | - Chang S. Nam
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Mufti Mahmud
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
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14
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Teneggi J, Chen X, Balu A, Barrett C, Grisolia G, Lucia U, Dzakpasu R. Entropy estimation within in vitro neural-astrocyte networks as a measure of development instability. Phys Rev E 2021; 103:042412. [PMID: 34005938 DOI: 10.1103/physreve.103.042412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/01/2021] [Indexed: 11/07/2022]
Abstract
The brain demands a significant fraction of the energy budget in an organism; in humans, it accounts for 2% of the body mass, but utilizes 20% of the total energy metabolized. This is due to the large load required for information processing; spiking demands from neurons are high but are a key component to understanding brain functioning. Astrocytic brain cells contribute to the healthy functioning of brain circuits by mediating neuronal network energy and facilitating the formation and stabilization of synaptic connectivity. During development, spontaneous activity influences synaptic formation, shaping brain circuit construction, and adverse astrocyte mutations can lead to pathological processes impacting cognitive impairment due to inefficiencies in network spiking activity. We have developed a measure that quantifies information stability within in vitro networks consisting of mixed neural-astrocyte cells. Brain cells were harvested from mice with mutations to a gene associated with the strongest known genetic risk factor for Alzheimer's disease, APOE. We calculate energy states of the networks and using these states, we present an entropy-based measure to assess changes in information stability over time. We show that during development, stability profiles of spontaneous network activity are modified by exogenous astrocytes and that network stability, in terms of the rate of change of entropy, is allele dependent.
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Affiliation(s)
- Jacopo Teneggi
- Department of Mechanical Engineering, Politecnico di Torino, Torino 10129, Italy; Department of Physics, Georgetown University, Washington, District of Columbia, 20057, USA; and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Xin Chen
- Department of Physics, Georgetown University, Washington, District of Columbia 20057, USA
| | - Alan Balu
- Department of Chemistry, Georgetown University, Washington, District of Columbia 20057, USA
| | - Connor Barrett
- Department of Physics, Georgetown University, Washington, District of Columbia 20057, USA
| | - Giulia Grisolia
- Department of Energy "Galileo Ferraris," Politecnico di Torino, Torino 10129, Italy
| | | | - Rhonda Dzakpasu
- Department of Physics, Georgetown University, Washington, District of Columbia 20057, USA and Department of Pharmacology and Physiology, Georgetown University Medical Center, Washington, District of Columbia 20057, USA
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