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Fernández Santoro EM, Karim A, Warnaar P, De Zeeuw CI, Badura A, Negrello M. Purkinje cell models: past, present and future. Front Comput Neurosci 2024; 18:1426653. [PMID: 39049990 PMCID: PMC11266113 DOI: 10.3389/fncom.2024.1426653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70's, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.
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Affiliation(s)
| | - Arun Karim
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
| | - Pascal Warnaar
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Chris I. De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | | | - Mario Negrello
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
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2
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Bruel A, Abadía I, Collin T, Sakr I, Lorach H, Luque NR, Ros E, Ijspeert A. The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation. PLoS Comput Biol 2024; 20:e1011008. [PMID: 38166093 PMCID: PMC10786408 DOI: 10.1371/journal.pcbi.1011008] [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: 03/08/2023] [Revised: 01/12/2024] [Accepted: 12/12/2023] [Indexed: 01/04/2024] Open
Abstract
Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.
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Affiliation(s)
- Alice Bruel
- Biorobotics Laboratory, EPFL, Lausanne, Switzerland
| | - Ignacio Abadía
- Research Centre for Information and Communication Technologies, Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | | | - Icare Sakr
- NeuroRestore, EPFL, Lausanne, Switzerland
| | | | - Niceto R. Luque
- Research Centre for Information and Communication Technologies, Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
| | - Eduardo Ros
- Research Centre for Information and Communication Technologies, Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain
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3
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Olkhova EA, Smith LA, Bradshaw C, Gorman GS, Erskine D, Ng YS. Neurological Phenotypes in Mouse Models of Mitochondrial Disease and Relevance to Human Neuropathology. Int J Mol Sci 2023; 24:ijms24119698. [PMID: 37298649 DOI: 10.3390/ijms24119698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
Mitochondrial diseases represent the most common inherited neurometabolic disorders, for which no effective therapy currently exists for most patients. The unmet clinical need requires a more comprehensive understanding of the disease mechanisms and the development of reliable and robust in vivo models that accurately recapitulate human disease. This review aims to summarise and discuss various mouse models harbouring transgenic impairments in genes that regulate mitochondrial function, specifically their neurological phenotype and neuropathological features. Ataxia secondary to cerebellar impairment is one of the most prevalent neurological features of mouse models of mitochondrial dysfunction, consistent with the observation that progressive cerebellar ataxia is a common neurological manifestation in patients with mitochondrial disease. The loss of Purkinje neurons is a shared neuropathological finding in human post-mortem tissues and numerous mouse models. However, none of the existing mouse models recapitulate other devastating neurological phenotypes, such as refractory focal seizures and stroke-like episodes seen in patients. Additionally, we discuss the roles of reactive astrogliosis and microglial reactivity, which may be driving the neuropathology in some of the mouse models of mitochondrial dysfunction, as well as mechanisms through which cellular death may occur, beyond apoptosis, in neurons undergoing mitochondrial bioenergy crisis.
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Affiliation(s)
- Elizaveta A Olkhova
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Laura A Smith
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Carla Bradshaw
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Gráinne S Gorman
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK
- NIHR Newcastle Biomedical Research Centre, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
| | - Daniel Erskine
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- NIHR Newcastle Biomedical Research Centre, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
| | - Yi Shiau Ng
- Wellcome Centre for Mitochondrial Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- NHS Highly Specialised Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK
- NIHR Newcastle Biomedical Research Centre, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
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4
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Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory-motor adaptation. Neural Netw 2022; 155:422-438. [DOI: 10.1016/j.neunet.2022.08.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/16/2022] [Accepted: 08/24/2022] [Indexed: 11/18/2022]
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5
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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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6
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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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7
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Transcranial direct current stimulation of cerebellum alters spiking precision in cerebellar cortex: A modeling study of cellular responses. PLoS Comput Biol 2021; 17:e1009609. [PMID: 34882680 PMCID: PMC8691604 DOI: 10.1371/journal.pcbi.1009609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 12/21/2021] [Accepted: 11/02/2021] [Indexed: 01/13/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) of the cerebellum has rapidly raised interest but the effects of tDCS on cerebellar neurons remain unclear. Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. In this study, we combine MRI-based segmentation of the cerebellum and a finite element model of the tDCS-induced electric field (EF) inside the cerebellum to determine the field imposed on the cerebellar neurons throughout the region. We then pair the EF with multicompartment models of the Purkinje cell (PC), deep cerebellar neuron (DCN), and granule cell (GrC) and quantify the acute response of these neurons under various orientations, physiological conditions, and sequences of presynaptic stimuli. We show that cerebellar tDCS significantly modulates the postsynaptic spiking precision of the PC, which is expressed as a change in the spike count and timing in response to presynaptic stimuli. tDCS has modest effects, instead, on the PC tonic firing at rest and on the postsynaptic activity of DCN and GrC. In Purkinje cells, anodal tDCS shortens the repolarization phase following complex spikes (-14.7 ± 6.5% of baseline value, mean ± S.D.; max: -22.7%) and promotes burstiness with longer bursts compared to resting conditions. Cathodal tDCS, instead, promotes irregular spiking by enhancing somatic excitability and significantly prolongs the repolarization after complex spikes compared to baseline (+37.0 ± 28.9%, mean ± S.D.; max: +84.3%). tDCS-induced changes to the repolarization phase and firing pattern exceed 10% of the baseline values in Purkinje cells covering up to 20% of the cerebellar cortex, with the effects being distributed along the EF direction and concentrated in the area under the electrode over the cerebellum. Altogether, the acute effects of tDCS on cerebellum mainly focus on Purkinje cells and modulate the precision of the response to synaptic stimuli, thus having the largest impact when the cerebellar cortex is active. Since the spatiotemporal precision of the PC spiking is critical to learning and coordination, our results suggest cerebellar tDCS as a viable therapeutic option for disorders involving cerebellar hyperactivity such as ataxia. Transcranial direct current stimulation (tDCS) of the cerebellum is gaining momentum as a neuromodulation tool for the treatment of neurological diseases like movement disorders. Nonetheless, the response of cells in the cerebellum to tDCS is unclear and hardly generalizes from our understanding of tDCS of the cerebral cortex. We use computational models to investigate the response of several types of cerebellar neurons to the electric field induced by tDCS and show that, differently from the cerebral cortex, tDCS has significant acute effects on the cerebellar cortex. These effects (i) primarily alter the way Purkinje cells encode synaptic stimuli from the molecular layer and (ii) can help hyperactive cells regain postsynaptic spiking precision. Since the spatiotemporal precision of the Purkinje cell spiking is critical to learning and coordination, the study shows how tDCS can operate at the cellular level to treat movement disorders like tremor and ataxia.
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8
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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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9
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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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10
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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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11
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The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. J Neurosci 2021; 41:1850-1863. [PMID: 33452223 PMCID: PMC7939085 DOI: 10.1523/jneurosci.1719-20.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 12/01/2022] Open
Abstract
Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analog signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing. SIGNIFICANCE STATEMENT Understanding how neurons process information is a fundamental question in neuroscience. Purkinje cells (PCs) were traditionally regarded as linear point neurons. We used computational modeling to unveil their electrophysiological properties underlying the multiplexed coding strategy that is observed during behaviors. We demonstrate that increasing input intensity triggers localized dendritic spikes, shifting PCs from linear rate-coders to burst-pause timing-coders. Both coding strategies work at the level of individual dendritic branches. Our work suggests that PCs have the ability to implement branch-specific multiplexed coding at the cellular level, thereby increasing the capacity of cerebellar coding and learning.
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Anderson SR, Porrill J, Dean P. World Statistics Drive Learning of Cerebellar Internal Models in Adaptive Feedback Control: A Case Study Using the Optokinetic Reflex. Front Syst Neurosci 2020; 14:11. [PMID: 32269515 PMCID: PMC7111124 DOI: 10.3389/fnsys.2020.00011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 02/07/2020] [Indexed: 01/06/2023] Open
Abstract
The cerebellum is widely implicated in having an important role in adaptive motor control. Many of the computational studies on cerebellar motor control to date have focused on the associated architecture and learning algorithms in an effort to further understand cerebellar function. In this paper we switch focus to the signals driving cerebellar adaptation that arise through different motor behavior. To do this, we investigate computationally the contribution of the cerebellum to the optokinetic reflex (OKR), a visual feedback control scheme for image stabilization. We develop a computational model of the adaptation of the cerebellar response to the world velocity signals that excite the OKR (where world velocity signals are used to emulate head velocity signals when studying the OKR in head-fixed experimental laboratory conditions). The results show that the filter learnt by the cerebellar model is highly dependent on the power spectrum of the colored noise world velocity excitation signal. Thus, the key finding here is that the cerebellar filter is determined by the statistics of the OKR excitation signal.
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Affiliation(s)
- Sean R. Anderson
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - John Porrill
- Department of Psychology, University of Sheffield, Sheffield, United Kingdom
| | - Paul Dean
- Department of Psychology, University of Sheffield, Sheffield, United Kingdom
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13
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Geminiani A, Pedrocchi A, D'Angelo E, Casellato C. Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties. Front Comput Neurosci 2019; 13:68. [PMID: 31632258 PMCID: PMC6779816 DOI: 10.3389/fncom.2019.00068] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 09/10/2019] [Indexed: 12/14/2022] Open
Abstract
Sensorimotor signals are integrated and processed by the cerebellar circuit to predict accurate control of actions. In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). EGLIF models specifically tuned for each neuron type were embedded into an olivocerebellar scaffold reproducing realistic spatial organization and physiological convergence and divergence ratios of connections. In order to emulate the circuit involved in an eye blink response to two associated stimuli, we modeled two adjacent olivocerebellar microcomplexes with a common mossy fiber input but different climbing fiber inputs (either on or off). EGLIF-SNN model simulations revealed the emergence of fundamental response properties in Purkinje cells (burst-pause) and deep nuclei cells (pause-burst) similar to those reported in vivo. The expression of these properties depended on the specific activation of climbing fibers in the microcomplexes and did not emerge with scaffold models using simplified point neurons. This result supports the importance of embedding SNNs with realistic neuronal dynamics and appropriate connectivity and anticipates the scale-up of EGLIF-SNN and the embedding of plasticity rules required to investigate cerebellar functioning at multiple scales.
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Affiliation(s)
- Alice Geminiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alessandra Pedrocchi
- NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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14
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Zang Y, De Schutter E. Climbing Fibers Provide Graded Error Signals in Cerebellar Learning. Front Syst Neurosci 2019; 13:46. [PMID: 31572132 PMCID: PMC6749063 DOI: 10.3389/fnsys.2019.00046] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 08/19/2019] [Indexed: 11/13/2022] Open
Abstract
The cerebellum plays a critical role in coordinating and learning complex movements. Although its importance has been well recognized, the mechanisms of learning remain hotly debated. According to the classical cerebellar learning theory, depression of parallel fiber synapses instructed by error signals from climbing fibers, drives cerebellar learning. The uniqueness of long-term depression (LTD) in cerebellar learning has been challenged by evidence showing multi-site synaptic plasticity. In Purkinje cells, long-term potentiation (LTP) of parallel fiber synapses is now well established and it can be achieved with or without climbing fiber signals, making the role of climbing fiber input more puzzling. The central question is how individual Purkinje cells extract global errors based on climbing fiber input. Previous data seemed to demonstrate that climbing fibers are inefficient instructors, because they were thought to carry “binary” error signals to individual Purkinje cells, which significantly constrains the efficiency of cerebellar learning in several regards. In recent years, new evidence has challenged the traditional view of “binary” climbing fiber responses, suggesting that climbing fibers can provide graded information to efficiently instruct individual Purkinje cells to learn. Here we review recent experimental and theoretical progress regarding modulated climbing fiber responses in Purkinje cells. Analog error signals are generated by the interaction of varying climbing fibers inputs with simultaneous other synaptic input and with firing states of targeted Purkinje cells. Accordingly, the calcium signals which trigger synaptic plasticity can be graded in both amplitude and spatial range to affect the learning rate and even learning direction. We briefly discuss how these new findings complement the learning theory and help to further our understanding of how the cerebellum works.
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Affiliation(s)
- Yunliang Zang
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Erik De Schutter
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
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