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Geminiani A, Casellato C, Boele HJ, Pedrocchi A, De Zeeuw CI, D’Angelo E. Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning. PLoS Comput Biol 2024; 20:e1011277. [PMID: 38574161 PMCID: PMC11060558 DOI: 10.1371/journal.pcbi.1011277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 04/30/2024] [Accepted: 02/12/2024] [Indexed: 04/06/2024] Open
Abstract
According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.
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Affiliation(s)
- Alice Geminiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Henk-Jan Boele
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands
- Neuroscience Institute, Princeton University, Washington Road, Princeton, New Jersey, United States of America
| | - Alessandra Pedrocchi
- NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Chris I. De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
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2
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Vijayan A, Diwakar S. A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction. Front Neurosci 2022; 16:909146. [DOI: 10.3389/fnins.2022.909146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/02/2022] [Indexed: 11/29/2022] Open
Abstract
Spiking neural networks were introduced to understand spatiotemporal information processing in neurons and have found their application in pattern encoding, data discrimination, and classification. Bioinspired network architectures are considered for event-driven tasks, and scientists have looked at different theories based on the architecture and functioning. Motor tasks, for example, have networks inspired by cerebellar architecture where the granular layer recodes sparse representations of the mossy fiber (MF) inputs and has more roles in motor learning. Using abstractions from cerebellar connections and learning rules of deep learning network (DLN), patterns were discriminated within datasets, and the same algorithm was used for trajectory optimization. In the current work, a cerebellum-inspired spiking neural network with dynamics of cerebellar neurons and learning mechanisms attributed to the granular layer, Purkinje cell (PC) layer, and cerebellar nuclei interconnected by excitatory and inhibitory synapses was implemented. The model’s pattern discrimination capability was tested for two tasks on standard machine learning (ML) datasets and on following a trajectory of a low-cost sensor-free robotic articulator. Tuned for supervised learning, the pattern classification capability of the cerebellum-inspired network algorithm has produced more generalized models than data-specific precision models on smaller training datasets. The model showed an accuracy of 72%, which was comparable to standard ML algorithms, such as MLP (78%), Dl4jMlpClassifier (64%), RBFNetwork (71.4%), and libSVM-linear (85.7%). The cerebellar model increased the network’s capability and decreased storage, augmenting faster computations. Additionally, the network model could also implicitly reconstruct the trajectory of a 6-degree of freedom (DOF) robotic arm with a low error rate by reconstructing the kinematic parameters. The variability between the actual and predicted trajectory points was noted to be ± 3 cm (while moving to a position in a cuboid space of 25 × 30 × 40 cm). Although a few known learning rules were implemented among known types of plasticity in the cerebellum, the network model showed a generalized processing capability for a range of signals, modulating the data through the interconnected neural populations. In addition to potential use on sensor-free or feed-forward based controllers for robotic arms and as a generalized pattern classification algorithm, this model adds implications to motor learning theory.
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3
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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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5
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Geminiani A, Mockevičius A, D’Angelo E, Casellato C. Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model. Front Syst Neurosci 2022; 16:919761. [PMID: 35782305 PMCID: PMC9243665 DOI: 10.3389/fnsys.2022.919761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive movements, postures, or both. Although dystonia is traditionally associated with basal ganglia dysfunction, recent evidence has been pointing to a role of the cerebellum, a brain area involved in motor control and learning. Cerebellar abnormalities have been correlated with dystonia but their potential causative role remains elusive. Here, we simulated the cerebellar input-output relationship with high-resolution computational modeling. We used a data-driven cerebellar Spiking Neural Network and simulated a cerebellum-driven associative learning task, Eye-Blink Classical Conditioning (EBCC), which is characteristically altered in relation to cerebellar lesions in several pathologies. In control simulations, input stimuli entrained characteristic network dynamics and induced synaptic plasticity along task repetitions, causing a progressive spike suppression in Purkinje cells with consequent facilitation of deep cerebellar nuclei cells. These neuronal processes caused a progressive acquisition of eyelid Conditioned Responses (CRs). Then, we modified structural or functional local neural features in the network reproducing alterations reported in dystonic mice. Either reduced olivocerebellar input or aberrant Purkinje cell burst-firing resulted in abnormal learning curves imitating the dysfunctional EBCC motor responses (in terms of CR amount and timing) of dystonic mice. These behavioral deficits might be due to altered temporal processing of sensorimotor information and uncoordinated control of muscle contractions. Conversely, an imbalance of excitatory and inhibitory synaptic densities on Purkinje cells did not reflect into significant EBCC deficit. The present work suggests that only certain types of alterations, including reduced olivocerebellar input and aberrant PC burst-firing, are compatible with the EBCC changes observed in dystonia, indicating that some cerebellar lesions can have a causative role in the pathogenesis of symptoms.
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Affiliation(s)
- Alice Geminiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Aurimas Mockevičius
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Claudia Casellato,
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6
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Antonietti A, Geminiani A, Negri E, D'Angelo E, Casellato C, Pedrocchi A. Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System. Front Neurorobot 2022; 16:817948. [PMID: 35770277 PMCID: PMC9234954 DOI: 10.3389/fnbot.2022.817948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.
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Affiliation(s)
- Alberto Antonietti
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- *Correspondence: Alberto Antonietti
| | - Alice Geminiani
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Edoardo Negri
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Egidio D'Angelo
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia Casellato
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alessandra Pedrocchi
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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7
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Migalev AS, Vigasina KD, Gotovtsev PM. A review of motor neural system robotic modeling approaches and instruments. BIOLOGICAL CYBERNETICS 2022; 116:271-306. [PMID: 35041073 DOI: 10.1007/s00422-021-00918-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
In this review, we are considering an actively developing tool in neuroscience-robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.
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Affiliation(s)
- Alexander S Migalev
- National Research Center "Kurchatov Intitute", 1, Akademika Kurchatova pl., Moscow, 123182, Russia
| | - Kristina D Vigasina
- Institute of Higher Nervous Activity and Neurophysiology of RAS, 5A, Butlerova st., Moscow, 117485, Russia
| | - Pavel M Gotovtsev
- National Research Center "Kurchatov Intitute", 1, Akademika Kurchatova pl., Moscow, 123182, Russia
- Moscow Institute of Physics and Technology 9, Institutsky per., Dolgoprudny, Moscow Region, 141701, Russian Federation
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8
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Computational epidemiology study of homeostatic compensation during sensorimotor aging. Neural Netw 2021; 146:316-333. [PMID: 34923219 DOI: 10.1016/j.neunet.2021.11.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/26/2021] [Accepted: 11/24/2021] [Indexed: 11/20/2022]
Abstract
The vestibulo-ocular reflex (VOR) stabilizes vision during head motion. Age-related changes of vestibular neuroanatomical properties predict a linear decay of VOR function. Nonetheless, human epidemiological data show a stable VOR function across the life span. In this study, we model cerebellum-dependent VOR adaptation to relate structural and functional changes throughout aging. We consider three neurosynaptic factors that may codetermine VOR adaptation during aging: the electrical coupling of inferior olive neurons, the long-term spike timing-dependent plasticity at parallel fiber - Purkinje cell synapses and mossy fiber - medial vestibular nuclei synapses, and the intrinsic plasticity of Purkinje cell synapses Our cross-sectional aging analyses suggest that long-term plasticity acts as a global homeostatic mechanism that underpins the stable temporal profile of VOR function. The results also suggest that the intrinsic plasticity of Purkinje cell synapses operates as a local homeostatic mechanism that further sustains the VOR at older ages. Importantly, the computational epidemiology approach presented in this study allows discrepancies among human cross-sectional studies to be understood in terms of interindividual variability in older individuals. Finally, our longitudinal aging simulations show that the amount of residual fibers coding for the peak and trough of the VOR cycle constitutes a predictive hallmark of VOR trajectories over a lifetime.
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9
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Abadía I, Naveros F, Ros E, Carrillo RR, Luque NR. A cerebellar-based solution to the nondeterministic time delay problem in robotic control. Sci Robot 2021; 6:eabf2756. [PMID: 34516748 DOI: 10.1126/scirobotics.abf2756] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Ignacio Abadía
- Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Francisco Naveros
- Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Computer School, Department of Architecture and Technology of Informatics Systems, Polytechnic University of Madrid, Madrid, Spain
| | - Eduardo Ros
- Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Richard R Carrillo
- Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Niceto R Luque
- Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain
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10
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Bogdan PA, Marcinnò B, Casellato C, Casali S, Rowley AGD, Hopkins M, Leporati F, D'Angelo E, Rhodes O. Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum. Front Cell Neurosci 2021; 15:622870. [PMID: 34135732 PMCID: PMC8202688 DOI: 10.3389/fncel.2021.622870] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 03/24/2021] [Indexed: 11/25/2022] Open
Abstract
This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms.
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Affiliation(s)
- Petruţ A Bogdan
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Beatrice Marcinnò
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Claudia Casellato
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Stefano Casali
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Andrew G D Rowley
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Michael Hopkins
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Francesco Leporati
- Department of Electrical, Computer and Biomedical Engineering, 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
| | - Oliver Rhodes
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
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11
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Abadia I, Naveros F, Garrido JA, Ros E, Luque NR. On Robot Compliance: A Cerebellar Control Approach. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2476-2489. [PMID: 31647453 DOI: 10.1109/tcyb.2019.2945498] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.
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12
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Kalidindi HT, Vannucci L, Laschi C, Falotico E. Cerebellar adaptive mechanisms explain the optimal control of saccadic eye movements. BIOINSPIRATION & BIOMIMETICS 2020; 16:016004. [PMID: 33150874 DOI: 10.1088/1748-3190/abae7f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Cerebellar synaptic plasticity is vital for adaptability and fine tuning of goal-directed movements. The perceived sensory errors between desired and actual movement outcomes are commonly considered to induce plasticity in the cerebellar synapses, with an objective to improve desirability of the executed movements. In rapid goal-directed eye movements called saccades, the only available sensory feedback is the direction of reaching error information received only at end of the movement. Moreover, this sensory error dependent plasticity can only improve the accuracy of the movements, while ignoring other essential characteristics such as reaching in minimum-time. In this work we propose a rate based, cerebellum inspired adaptive filter model to address refinement of both accuracy and movement-time of saccades. We use optimal control approach in conjunction with information constraints posed by the cerebellum to derive bio-plausible supervised plasticity rules. We implement and validate this bio-inspired scheme on a humanoid robot. We found out that, separate plasticity mechanisms in the model cerebellum separately control accuracy and movement-time. These plasticity mechanisms ensure that optimal saccades are produced by just receiving the direction of end reaching error as an evaluative signal. Furthermore, the model emulates encoding in the cerebellum of movement kinematics as observed in biological experiments.
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Affiliation(s)
- Hari Teja Kalidindi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Italy
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13
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Israely S, Leisman G. Can neuromodulation techniques optimally exploit cerebello-thalamo-cortical circuit properties to enhance motor learning post-stroke? Rev Neurosci 2020; 30:821-837. [PMID: 31194694 DOI: 10.1515/revneuro-2019-0008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 04/11/2019] [Indexed: 02/07/2023]
Abstract
Individuals post-stroke sustain motor deficits years after the stroke. Despite recent advancements in the applications of non-invasive brain stimulation techniques and Deep Brain Stimulation in humans, there is a lack of evidence supporting their use for rehabilitation after brain lesions. Non-invasive brain stimulation is already in use for treating motor deficits in individuals with Parkinson's disease and post-stroke. Deep Brain Stimulation has become an established treatment for individuals with movement disorders, such as Parkinson's disease, essential tremor, epilepsy, cerebral palsy and dystonia. It has also been utilized for the treatment of Tourette's syndrome, Alzheimer's disease and neuropsychiatric conditions such as obsessive-compulsive disorder, major depression and anorexia nervosa. There exists growing scientific knowledge from animal studies supporting the use of Deep Brain Stimulation to enhance motor recovery after brain damage. Nevertheless, these results are currently not applicable to humans. This review details the current literature supporting the use of these techniques to enhance motor recovery, both from human and animal studies, aiming to encourage development in this domain.
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Affiliation(s)
- Sharon Israely
- Department of Medical Neurobiology, IMRIC and ELSC, The Hebrew University, Hadassah Medical School, Jerusalem 9112102, Israel
| | - Gerry Leisman
- Department of Physiotherapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel.,Universidad de Ciencias Médicas Instituto de Neurología y Neurocirugía, Neurofisiología Clinica, Havana, Cuba
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14
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Gandolfi D, Bigiani A, Porro CA, Mapelli J. Inhibitory Plasticity: From Molecules to Computation and Beyond. Int J Mol Sci 2020; 21:ijms21051805. [PMID: 32155701 PMCID: PMC7084224 DOI: 10.3390/ijms21051805] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/28/2020] [Accepted: 03/03/2020] [Indexed: 11/17/2022] Open
Abstract
Synaptic plasticity is the cellular and molecular counterpart of learning and memory and, since its first discovery, the analysis of the mechanisms underlying long-term changes of synaptic strength has been almost exclusively focused on excitatory connections. Conversely, inhibition was considered as a fixed controller of circuit excitability. Only recently, inhibitory networks were shown to be finely regulated by a wide number of mechanisms residing in their synaptic connections. Here, we review recent findings on the forms of inhibitory plasticity (IP) that have been discovered and characterized in different brain areas. In particular, we focus our attention on the molecular pathways involved in the induction and expression mechanisms leading to changes in synaptic efficacy, and we discuss, from the computational perspective, how IP can contribute to the emergence of functional properties of brain circuits.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
- Department of Brain and behavioral sciences, University of Pavia, 27100 Pavia, Italy
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
| | - Carlo Adolfo Porro
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences and Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (A.B.); (C.A.P.)
- Correspondence: ; Tel.: +39-059-205- 5459
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Capolei MC, Andersen NA, Lund HH, Falotico E, Tolu S. A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2019.2943818] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Capolei MC, Angelidis E, Falotico E, Lund HH, Tolu S. A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment. Front Neurorobot 2019; 13:70. [PMID: 31555117 PMCID: PMC6722230 DOI: 10.3389/fnbot.2019.00070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 08/12/2019] [Indexed: 11/13/2022] Open
Abstract
One of the big challenges in robotics is to endow agents with autonomous and adaptive capabilities. With this purpose, we embedded a cerebellum-based control system into a humanoid robot that becomes capable of handling dynamical external and internal complexity. The cerebellum is the area of the brain that coordinates and predicts the body movements throughout the body-environment interactions. Different biologically plausible cerebellar models are available in literature and have been employed for motor learning and control of simplified objects. We built the canonical cerebellar microcircuit by combining machine learning and computational neuroscience techniques. The control system is composed of the adaptive cerebellar module and a classic control method; their combination allows a fast adaptive learning and robust control of the robotic movements when external disturbances appear. The control structure is built offline, but the dynamic parameters are learned during an online-phase training. The aforementioned adaptive control system has been tested in the Neuro-robotics Platform with the virtual humanoid robot iCub. In the experiment, the robot iCub has to balance with the hand a table with a ball running on it. In contrast with previous attempts of solving this task, the proposed neural controller resulted able to quickly adapt when the internal and external conditions change. Our bio-inspired and flexible control architecture can be applied to different robotic configurations without an excessive tuning of the parameters or customization. The cerebellum-based control system is indeed able to deal with changing dynamics and interactions with the environment. Important insights regarding the relationship between the bio-inspired control system functioning and the complexity of the task to be performed are obtained.
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Affiliation(s)
- Marie Claire Capolei
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
| | - Emmanouil Angelidis
- Landesforschungsinstitut des Freistaats Bayern, An-Institut, Technical University of Munich, Munich, Germany
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Henrik Hautop Lund
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
| | - Silvia Tolu
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
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Solouki S, Bahrami F, Janahmadi M. The Concept of Transmission Coefficient Among Different Cerebellar Layers: A Computational Tool for Analyzing Motor Learning. Front Neural Circuits 2019; 13:54. [PMID: 31507382 PMCID: PMC6718712 DOI: 10.3389/fncir.2019.00054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 07/29/2019] [Indexed: 11/13/2022] Open
Abstract
High-fidelity regulation of information transmission among cerebellar layers is mainly provided by synaptic plasticity. Therefore, determining the regulatory foundations of synaptic plasticity in the cerebellum and translating them to behavioral output are of great importance. To date, many experimental studies have been carried out in order to clarify the effect of synaptic defects, while targeting a specific signaling pathway in the cerebellar function. However, the contradictory results of these studies at the behavioral level further add to the ambiguity of the problem. Information transmission through firing rate changes in populations of interconnected neurons is one of the most widely accepted principles of neural coding. In this study, while considering the efficacy of synaptic interactions among the cerebellar layers, we propose a firing rate model to realize the concept of transmission coefficient. Thereafter, using a computational approach, we test the effect of different values of transmission coefficient on the gain adaptation of a cerebellar-dependent motor learning task. In conformity with the behavioral data, the proposed model can accurately predict that disruption in different forms of synaptic plasticity does not have the same effect on motor learning. Specifically, impairment in training mechanisms, like in the train-induced LTD in parallel fiber-Purkinje cell synapses, has a significant negative impact on all aspects of learning, including memory formation, transfer, and consolidation, although it does not disrupt basic motor performance. In this regard, the overinduction of parallel fiber-molecular layer interneuron LTP could not prevent motor learning impairment, despite its vital role in preserving the robustness of basic motor performance. In contrast, impairment in plasticity induced by interneurons and background activity of climbing fibers is partly compensable through overinduction of train-induced parallel fiber-Purkinje cell LTD. Additionally, blockade of climbing fiber signaling to the cerebellar cortex, referred to as olivary system lesion, shows the most destructive effect on both motor learning and basic motor performance. Overall, the obtained results from the proposed computational framework are used to provide a map from procedural motor memory formation in the cerebellum. Certainly, the generalization of this concept to other multi-layered networks of the brain requires more physiological and computational researches.
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Affiliation(s)
- Saeed Solouki
- Control and Intelligent Processing Center of Excellence, Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Fariba Bahrami
- Control and Intelligent Processing Center of Excellence, Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahyar Janahmadi
- Department of Physiology, Neuroscience Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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18
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Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A. Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation. PLoS Comput Biol 2019; 15:e1006298. [PMID: 30860991 PMCID: PMC6430425 DOI: 10.1371/journal.pcbi.1006298] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 03/22/2019] [Accepted: 01/08/2019] [Indexed: 11/25/2022] Open
Abstract
Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation. Cerebellar Purkinje cells regulate accurate eye movement coordination. However, it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic, high frequency bursting, and post-complex spike pauses. We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation. A biophysical model of Purkinje cell is at the core of a spiking network model, which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites. We show that Purkinje spike burst-pause dynamics are critical for (1) gating the vestibular-motor response association during VOR acquisition; (2) mediating the LTD/LTP balance for VOR consolidation; (3) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping.
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Affiliation(s)
- Niceto R. Luque
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
- * E-mail: (NRL); (AA)
| | - Francisco Naveros
- Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain
| | - Richard R. Carrillo
- Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, CITIC-University of Granada, Granada, Spain
| | - Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
- * E-mail: (NRL); (AA)
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19
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Naveros F, Luque NR, Ros E, Arleo A. VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model. IEEE TRANSACTIONS ON CYBERNETICS 2019; 50:4744-4757. [PMID: 30835236 DOI: 10.1109/tcyb.2019.2899246] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental setup (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and RT operation. The RT supervisor module provides for incremental countermeasures that dynamically slow down or speed up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e., STDP mechanisms, spike propagation, and neural updates) to cope with the RT constraints imposed by the real robot operation. This neurorobotic experimental setup is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This paper underlies the need for a two-stage learning process to facilitate VOR acquisition.
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20
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Abstract
The cerebellum is a central brain structure deeply integrated into major loops with the cerebral cortex, brainstem, and spinal cord. The cerebellum shows a complex regional organization consisting of modules with sagittal orientation. The cerebellum takes part in motor control and its lesions cause a movement incoordination syndrome called ataxia. Recent observations also imply involvement of the cerebellum in cognition and executive control, with an impact on pathologies like dyslexia and autism. The cerebellum operates as a forward controller learning to predict the precise timing of correlated events. The physiologic mechanisms of cerebellar functioning are still the object of intense research. The signals entering the cerebellum through the mossy fibers are processed in the granular layer and transmitted to Purkinje cells, while a collateral pathway activates the deep cerebellar nuclei (DCN). Purkinje cells in turn inhibit DCN, so that the cerebellar cortex operates as a side loop controlling the DCN. Learning is now known to occur through synaptic plasticity at multiple synapses in the granular layer, molecular layer, and DCN, extending the original concept of the Motor Learning Theory that predicted a single form of plasticity at the synapse between parallel fibers and Purkinje cells under the supervision of climbing fibers deriving from the inferior olive. Coordination derives from the precise regulation of timing and gain in the different cerebellar modules. The investigation of cerebellar dynamics using advanced physiologic recordings and computational models is now providing new clues on how the cerebellar network performs its internal computations.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
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21
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A distributed cerebellar-inspired learning model for robotic arm control. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:929-932. [PMID: 29060025 DOI: 10.1109/embc.2017.8036977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The cerebellum plays a big role in motor control and motor coordination in mammals, especially for limbs control. Therefore, many cerebellar models were proposed to be applied in the field of robotic arm control. However, some problems exist in the current cerebellar modeling approach, such as lack of the expression of bio-characteristics, limited learning ability et al. Therefore, a distributed cerebellar-inspired learning model was proposed to mimic the physiology and anatomy features of the cerebellum. Meanwhile, this model could learn to adjust the motor command according to the error information provided by the inferior olive to achieve control goal. To test the performance of the cerebellar model, a robotic arm control system was implemented. The results showed that our model was able to complete the robotic arm control tasks successfully.
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22
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New insights into olivo-cerebellar circuits for learning from a small training sample. Curr Opin Neurobiol 2017; 46:58-67. [PMID: 28841437 DOI: 10.1016/j.conb.2017.07.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 07/26/2017] [Accepted: 07/27/2017] [Indexed: 11/24/2022]
Abstract
Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is mainly due to differences in the amount of training data, which is huge and small, respectively. Animals are not allowed with millions of the failed trials, which lead to injury and death. Humans fall only several thousand times before they balance and walk. We hypothesize that a unique closed-loop neural circuit formed by the Purkinje cells, the cerebellar deep nucleus and the inferior olive in and around the cerebellum and the highest density of gap junctions, which regulate synchronous activities of the inferior olive nucleus, are computational machinery for learning from a small sample. We discuss recent experimental and computational advances associated with this hypothesis.
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23
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Naveros F, Garrido JA, Carrillo RR, Ros E, Luque NR. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. Front Neuroinform 2017; 11:7. [PMID: 28223930 PMCID: PMC5293783 DOI: 10.3389/fninf.2017.00007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 01/18/2017] [Indexed: 12/12/2022] Open
Abstract
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.
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Affiliation(s)
- Francisco Naveros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Jesus A Garrido
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Richard R Carrillo
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain
| | - Niceto R Luque
- Vision Institute, Aging in Vision and Action LabParis, France; CNRS, INSERM, Pierre and Marie Curie UniversityParis, France
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Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage. J Neurosci 2017; 37:2809-2823. [PMID: 28188217 DOI: 10.1523/jneurosci.2079-16.2016] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 11/10/2016] [Accepted: 12/28/2016] [Indexed: 11/21/2022] Open
Abstract
Spike-timing-dependent plasticity (STDP) is a form of long-term synaptic plasticity exploiting the time relationship between postsynaptic action potentials (APs) and EPSPs. Surprisingly enough, very little was known about STDP in the cerebellum, although it is thought to play a critical role for learning appropriate timing of actions. We speculated that low-frequency oscillations observed in the granular layer may provide a reference for repetitive EPSP/AP phase coupling. Here we show that EPSP-spike pairing at 6 Hz can optimally induce STDP at the mossy fiber-granule cell synapse in rats. Spike timing-dependent long-term potentiation and depression (st-LTP and st-LTD) were confined to a ±25 ms time-window. Because EPSPs led APs in st-LTP while APs led EPSPs in st-LTD, STDP was Hebbian in nature. STDP occurred at 6-10 Hz but vanished >50 Hz or <1 Hz (where only LTP or LTD occurred). STDP disappeared with randomized EPSP/AP pairing or high intracellular Ca2+ buffering, and its sign was inverted by GABA-A receptor activation. Both st-LTP and st-LTD required NMDA receptors, but st-LTP also required reinforcing signals mediated by mGluRs and intracellular calcium stores. Importantly, st-LTP and st-LTD were significantly larger than LTP and LTD obtained by modulating the frequency and duration of mossy fiber bursts, probably because STDP expression involved postsynaptic in addition to presynaptic mechanisms. These results thus show that a Hebbian form of STDP occurs at the cerebellum input stage, providing the substrate for phase-dependent binding of mossy fiber spikes to repetitive theta-frequency cycles of granule cell activity.SIGNIFICANCE STATEMENT Long-term synaptic plasticity is a fundamental property of the brain, causing persistent modifications of neuronal communication thought to provide the cellular basis of learning and memory. The cerebellum is critical for learning the appropriate timing of sensorimotor behaviors, but whether and how appropriate spike patterns could drive long-term synaptic plasticity remained unknown. Here, we show that this can actually occur through a form of spike-timing-dependent plasticity (STDP) at the cerebellar inputs stage. Pairing presynaptic and postsynaptic spikes at 6-10 Hz reliably induced STDP at the mossy fiber-granule cell synapse, with potentiation and depression symmetrically distributed within a ±25 ms time window. Thus, STDP can bind plasticity to the mossy fiber burst phase with high temporal precision.
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D'Angelo E, Antonietti A, Casali S, Casellato C, Garrido JA, Luque NR, Mapelli L, Masoli S, Pedrocchi A, Prestori F, Rizza MF, Ros E. Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue. Front Cell Neurosci 2016; 10:176. [PMID: 27458345 PMCID: PMC4937064 DOI: 10.3389/fncel.2016.00176] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 06/23/2016] [Indexed: 11/13/2022] Open
Abstract
The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Alberto Antonietti
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Stefano Casali
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Claudia Casellato
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Jesus A Garrido
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
| | - Niceto Rafael Luque
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
| | - Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Alessandra Pedrocchi
- NearLab - NeuroEngineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
| | - Martina Francesca Rizza
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-BicoccaMilan, Italy
| | - Eduardo Ros
- Department of Computer Architecture and Technology, University of Granada Granada, Spain
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