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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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Bayat FK, Alp Mİ, Bostan S, Gülçür HÖ, Öztürk G, Güveniş A. An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs. Eur Biophys J 2022; 51:503-514. [PMID: 35930029 DOI: 10.1007/s00249-022-01613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/11/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.
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Affiliation(s)
- F Kemal Bayat
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
| | - M İkbal Alp
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Sevginur Bostan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - H Özcan Gülçür
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Gürkan Öztürk
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Albert Güveniş
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
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3
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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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Beaubois R, Khoyratee F, Branchereau P, Ikeuchi Y, Levi T. From real-time single to multicompartmental Hodgkin-Huxley neurons on FPGA for bio-hybrid systems. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:1602-1606. [PMID: 36083914 DOI: 10.1109/embc48229.2022.9871176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Modeling biological neural networks has been a field opening to major advances in our understanding of the mechanisms governing the functioning of the brain in normal and pathological conditions. The emergence of real-time neuromorphic platforms has been leading to a rising significance of bio-hybrid experiments as part of the development of neuromorphic biomedical devices such as neuroprosthesis. To provide a new tool for the neurological disorder characterization, we design real-time single and multicompartmental Hodgkin-Huxley neurons on FPGA. These neurons allow biological neural network emulation featuring improved accuracy through compartment modeling and show integration in bio-hybrid system thanks to its real-time dynamics.
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Yang S, Wang J, Hao X, Li H, Wei X, Deng B, Loparo KA. BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture. IEEE Trans Neural Netw Learn Syst 2022; 33:2801-2815. [PMID: 33428574 DOI: 10.1109/tnnls.2020.3045492] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.
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Aydin O, Passaro AP, Raman R, Spellicy SE, Weinberg RP, Kamm RD, Sample M, Truskey GA, Zartman J, Dar RD, Palacios S, Wang J, Tordoff J, Montserrat N, Bashir R, Saif MTA, Weiss R. Principles for the design of multicellular engineered living systems. APL Bioeng 2022; 6:010903. [PMID: 35274072 PMCID: PMC8893975 DOI: 10.1063/5.0076635] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 12/14/2022] Open
Abstract
Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.
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Affiliation(s)
| | - Austin P. Passaro
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia 30602, USA
| | - Ritu Raman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | | | - Robert P. Weinberg
- School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts 02115, USA
| | | | - Matthew Sample
- Center for Ethics and Law in the Life Sciences, Leibniz Universität Hannover, 30167 Hannover, Germany
| | - George A. Truskey
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Jeremiah Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Roy D. Dar
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Sebastian Palacios
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Jason Wang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jesse Tordoff
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Nuria Montserrat
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | | | - M. Taher A. Saif
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ron Weiss
- Author to whom correspondence should be addressed:
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Wei H, Shi R, Sun L, Yu H, Gong J, Liu C, Xu Z, Ni Y, Xu J, Xu W. Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics. Nat Commun 2021; 12:1068. [PMID: 33594066 DOI: 10.1038/s41467-021-21319-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain-computer interfaces.
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Abstract
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 "biohybrid" 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.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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Rich S, Hutt A, Skinner FK, Valiante TA, Lefebvre J. Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions. Sci Rep 2020; 10:15408. [PMID: 32958802 DOI: 10.1038/s41598-020-72335-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/26/2020] [Indexed: 12/29/2022] Open
Abstract
An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.
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Abstract
This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.
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Affiliation(s)
- Frank Klefenz
- Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany
| | - Tamas Harczos
- Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany
- Auditory Neuroscience and Optogenetics Laboratory, German Primate Center, Göttingen, Germany
- audifon GmbH & Co. KG, Kölleda, Germany
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12
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Mosbacher Y, Khoyratee F, Goldin M, Kanner S, Malakai Y, Silva M, Grassia F, Simon YB, Cortes J, Barzilai A, Levi T, Bonifazi P. Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation. Sci Rep 2020; 10:7512. [PMID: 32371937 DOI: 10.1038/s41598-020-63934-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 03/30/2020] [Indexed: 02/04/2023] Open
Abstract
Restoration of the communication between brain circuitry is a crucial step in the recovery of brain damage induced by traumatic injuries or neurological insults. In this work we present a study of real-time unidirectional communication between a spiking neuronal network (SNN) implemented on digital platform and an in-vitro biological neuronal network (BNN), generating similar spontaneous patterns of activity both spatial and temporal. The communication between the networks was established using patterned optogenetic stimulation via a modified digital light projector (DLP) receiving real-time input dictated by the spiking neurons' state. Each stimulation consisted of a binary image composed of 8 × 8 squares, representing the state of 64 excitatory neurons. The spontaneous and evoked activity of the biological neuronal network was recorded using a multi-electrode array in conjunction with calcium imaging. The image was projected in a sub-portion of the cultured network covered by a subset of the all electrodes. The unidirectional information transmission (SNN to BNN) is estimated using the similarity matrix of the input stimuli and output firing. Information transmission was studied in relation to the distribution of stimulus frequency and stimulus intensity, both regulated by the spontaneous dynamics of the SNN, and to the entrainment of the biological networks. We demonstrate that high information transfer from SNN to BNN is possible and identify a set of conditions under which such transfer can occur, namely when the spiking network synchronizations drive the biological synchronizations (entrainment) and in a linear regime response to the stimuli. This research provides further evidence of possible application of miniaturized SNN in future neuro-prosthetic devices for local replacement of injured micro-circuitries capable to communicate within larger brain networks.
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Abstract
One of the appealing cases of the neuromorphic research area is the implementation of biological neural networks. The current study offers Multiplierless Hodgkin-Huxley Model (MHHM). This modified model may reproduce various spiking behaviors, like the biological HH neurons, with high accuracy. The presented modified model, in comparison to the original HH model, due to its exact similarity to the original model, has more top performances in the case of FPGA saving and more achievable frequency (speed-up). In this approach, the proposed model has a 69 % saving in FPGA resources and also the maximum frequency of 85 MHz that is more than other similar works. In this modification, all spiking behaviors of the original model have been generated with low error calculations. To validate the MHHM neuron, this proposed model has been implemented on digital hardware FPGA. This approach demonstrates that the original HH model and the proposed model have high similarity in terms of higher performance and digital hardware cost reduction.
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Serb A, Corna A, George R, Khiat A, Rocchi F, Reato M, Maschietto M, Mayr C, Indiveri G, Vassanelli S, Prodromakis T. Memristive synapses connect brain and silicon spiking neurons. Sci Rep 2020; 10:2590. [PMID: 32098971 PMCID: PMC7042282 DOI: 10.1038/s41598-020-58831-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/21/2020] [Indexed: 11/09/2022] Open
Abstract
Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.
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Affiliation(s)
- Alexantrou Serb
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrea Corna
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Richard George
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Ali Khiat
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Federico Rocchi
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marco Reato
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marta Maschietto
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Christian Mayr
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - Stefano Vassanelli
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy.
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