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Pali E, D’Angelo E, Prestori F. Understanding Cerebellar Input Stage through Computational and Plasticity Rules. BIOLOGY 2024; 13:403. [PMID: 38927283 PMCID: PMC11200477 DOI: 10.3390/biology13060403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/21/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024]
Abstract
A central hypothesis concerning brain functioning is that plasticity regulates the signal transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, the granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns has been analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often accompanied by forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at the excitatory synapses formed by mossy fibers onto primary neuronal components (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we highlight the current understanding of the mechanisms and their functional implications for synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.
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Affiliation(s)
- Eleonora Pali
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (E.P.)
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (E.P.)
- Digital Neuroscience Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (E.P.)
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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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3
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Lorenzi RM, Geminiani A, Zerlaut Y, De Grazia M, Destexhe A, Gandini Wheeler-Kingshott CAM, Palesi F, Casellato C, D'Angelo E. A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics. PLoS Comput Biol 2023; 19:e1011434. [PMID: 37656758 PMCID: PMC10501640 DOI: 10.1371/journal.pcbi.1011434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 09/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.
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Affiliation(s)
| | - Alice Geminiani
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Yann Zerlaut
- Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | | | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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Mapelli J, Boiani GM, D’Angelo E, Bigiani A, Gandolfi D. Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer. Biomedicines 2022; 10:biomedicines10123185. [PMID: 36551941 PMCID: PMC9775043 DOI: 10.3390/biomedicines10123185] [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: 10/22/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.
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Affiliation(s)
- Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, Via Campi 287, University of Modena and Reggio Emilia, 41125 Modena, Italy
- Centre for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, 41125 Modena, Italy
- Correspondence: (J.M.); (D.G.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, Via Campi 287, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, Neurophysiology Unit, Via Forlanini 6, 27100 Pavia, Italy
- Brain Connectivity Center (BCC), IRCCS C. Mondino, Via Mondino 2, 27100 Pavia, Italy
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, Via Campi 287, University of Modena and Reggio Emilia, 41125 Modena, Italy
- Centre for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, Via Campi 287, University of Modena and Reggio Emilia, 41125 Modena, Italy
- Department of Brain and Behavioral Sciences, Neurophysiology Unit, Via Forlanini 6, 27100 Pavia, Italy
- Correspondence: (J.M.); (D.G.)
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Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit. Commun Biol 2022; 5:1240. [PMCID: PMC9663576 DOI: 10.1038/s42003-022-04213-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractThe cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior.
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Masoli S, Rizza MF, Tognolina M, Prestori F, D’Angelo E. Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation. Front Comput Neurosci 2022; 16:1006989. [PMID: 36387305 PMCID: PMC9649760 DOI: 10.3389/fncom.2022.1006989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Francesca Prestori,
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
- Egidio D’Angelo,
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A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits. Sci Rep 2022; 12:13864. [PMID: 35974119 PMCID: PMC9381785 DOI: 10.1038/s41598-022-18024-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/03/2022] [Indexed: 01/03/2023] Open
Abstract
The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models.
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D'Angelo E, Jirsa V. The quest for multiscale brain modeling. Trends Neurosci 2022; 45:777-790. [PMID: 35906100 DOI: 10.1016/j.tins.2022.06.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 06/21/2022] [Indexed: 01/07/2023]
Abstract
Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.
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Affiliation(s)
- Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, and Brain Connectivity Center, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy.
| | - Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 1106, Centre National de la Recherche Scientifique (CNRS), and University of Aix-Marseille, Marseille, France
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Gandolfi D, Puglisi FM, Boiani GM, Pagnoni G, Friston KJ, D'Angelo EU, Mapelli J. Emergence of associative learning in a neuromorphic inference network. J Neural Eng 2022; 19. [PMID: 35508120 DOI: 10.1088/1741-2552/ac6ca7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
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Affiliation(s)
- Daniela Gandolfi
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Francesco Maria Puglisi
- DIEF, Universita degli Studi di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena, MO, 41121, ITALY
| | - Giulia Maria Boiani
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Giuseppe Pagnoni
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Karl J Friston
- Institute of Neurology, University College London, 23 Queen Square, LONDON, WC1N 3BG, London, WC1N 3AR, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Egidio Ugo D'Angelo
- Department Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Pavia, Lombardia, 27100, ITALY
| | - Jonathan Mapelli
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, 41125, ITALY
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Gagliano G, Monteverdi A, Casali S, Laforenza U, Gandini Wheeler-Kingshott CAM, D’Angelo E, Mapelli L. Non-Linear Frequency Dependence of Neurovascular Coupling in the Cerebellar Cortex Implies Vasodilation-Vasoconstriction Competition. Cells 2022; 11:1047. [PMID: 35326498 PMCID: PMC8947624 DOI: 10.3390/cells11061047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/11/2022] [Accepted: 03/17/2022] [Indexed: 01/28/2023] Open
Abstract
Neurovascular coupling (NVC) is the process associating local cerebral blood flow (CBF) to neuronal activity (NA). Although NVC provides the basis for the blood oxygen level dependent (BOLD) effect used in functional MRI (fMRI), the relationship between NVC and NA is still unclear. Since recent studies reported cerebellar non-linearities in BOLD signals during motor tasks execution, we investigated the NVC/NA relationship using a range of input frequencies in acute mouse cerebellar slices of vermis and hemisphere. The capillary diameter increased in response to mossy fiber activation in the 6-300 Hz range, with a marked inflection around 50 Hz (vermis) and 100 Hz (hemisphere). The corresponding NA was recorded using high-density multi-electrode arrays and correlated to capillary dynamics through a computational model dissecting the main components of granular layer activity. Here, NVC is known to involve a balance between the NMDAR-NO pathway driving vasodilation and the mGluRs-20HETE pathway driving vasoconstriction. Simulations showed that the NMDAR-mediated component of NA was sufficient to explain the time course of the capillary dilation but not its non-linear frequency dependence, suggesting that the mGluRs-20HETE pathway plays a role at intermediate frequencies. These parallel control pathways imply a vasodilation-vasoconstriction competition hypothesis that could adapt local hemodynamics at the microscale bearing implications for fMRI signals interpretation.
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Affiliation(s)
- Giuseppe Gagliano
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
| | - Anita Monteverdi
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
- IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Stefano Casali
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
| | - Umberto Laforenza
- Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy;
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
- IRCCS Mondino Foundation, 27100 Pavia, Italy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1N3 BG, UK
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
- IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (G.G.); (A.M.); (S.C.); (C.A.M.G.W.-K.)
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Tognolina M, Monteverdi A, D’Angelo E. Discovering Microcircuit Secrets With Multi-Spot Imaging and Electrophysiological Recordings: The Example of Cerebellar Network Dynamics. Front Cell Neurosci 2022; 16:805670. [PMID: 35370553 PMCID: PMC8971197 DOI: 10.3389/fncel.2022.805670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 02/25/2022] [Indexed: 12/02/2022] Open
Abstract
The cerebellar cortex microcircuit is characterized by a highly ordered neuronal architecture having a relatively simple and stereotyped connectivity pattern. For a long time, this structural simplicity has incorrectly led to the idea that anatomical considerations would be sufficient to understand the dynamics of the underlying circuitry. However, recent experimental evidence indicates that cerebellar operations are much more complex than solely predicted by anatomy, due to the crucial role played by neuronal and synaptic properties. To be able to explore neuronal and microcircuit dynamics, advanced imaging, electrophysiological techniques and computational models have been combined, allowing us to investigate neuronal ensembles activity and to connect microscale to mesoscale phenomena. Here, we review what is known about cerebellar network organization, neural dynamics and synaptic plasticity and point out what is still missing and would require experimental assessments. We consider the available experimental techniques that allow a comprehensive assessment of circuit dynamics, including voltage and calcium imaging and extracellular electrophysiological recordings with multi-electrode arrays (MEAs). These techniques are proving essential to investigate the spatiotemporal pattern of activity and plasticity in the cerebellar network, providing new clues on how circuit dynamics contribute to motor control and higher cognitive functions.
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Affiliation(s)
- Marialuisa Tognolina
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- *Correspondence: Marialuisa Tognolina,
| | - Anita Monteverdi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
- Egidio D’Angelo,
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12
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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Gandolfi D, Boiani GM, Bigiani A, Mapelli J. Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders. Int J Mol Sci 2021; 22:4565. [PMID: 33925434 PMCID: PMC8123833 DOI: 10.3390/ijms22094565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
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14
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Kuriyama R, Casellato C, D'Angelo E, Yamazaki T. Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units. Front Cell Neurosci 2021; 15:623552. [PMID: 33897369 PMCID: PMC8058369 DOI: 10.3389/fncel.2021.623552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent role in reproducing and predicting the dynamics of the microcircuits. To reconstruct a microcircuit, one must choose neuron and synapse models, placements, connectivity, and numerical simulation methods according to anatomical and physiological constraints. For reconstruction and refinement, it is useful to be able to replace one module easily while leaving the others as they are. One way to achieve this is via a scaffolding approach, in which a simulation code is built on independent modules for placements, connections, and network simulations. Owing to the modularity of functions, this approach enables researchers to improve the performance of the entire simulation by simply replacing a problematic module with an improved one. Casali et al. (2019) developed a spiking network model of the cerebellar microcircuit using this approach, and while it reproduces electrophysiological properties of cerebellar neurons, it takes too much computational time. Here, we followed this scaffolding approach and replaced the simulation module with an accelerated version on graphics processing units (GPUs). Our cerebellar scaffold model ran roughly 100 times faster than the original version. In fact, our model is able to run faster than real time, with good weak and strong scaling properties. To demonstrate an application of real-time simulation, we implemented synaptic plasticity mechanisms at parallel fiber-Purkinje cell synapses, and carried out simulation of behavioral experiments known as gain adaptation of optokinetic response. We confirmed that the computer simulation reproduced experimental findings while being completed in real time. Actually, a computer simulation for 2 s of the biological time completed within 750 ms. These results suggest that the scaffolding approach is a promising concept for gradual development and refactoring of simulation codes for large-scale elaborate microcircuits. Moreover, a real-time version of the cerebellar scaffold model, which is enabled by parallel computing technology owing to GPUs, may be useful for large-scale simulations and engineering applications that require real-time signal processing and motor control.
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Affiliation(s)
- Rin Kuriyama
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Claudia Casellato
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Tadashi Yamazaki
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
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15
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The effects of the general anesthetic sevoflurane on neurotransmission: an experimental and computational study. Sci Rep 2021; 11:4335. [PMID: 33619298 PMCID: PMC7900247 DOI: 10.1038/s41598-021-83714-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 02/01/2021] [Indexed: 11/08/2022] Open
Abstract
The brain functions can be reversibly modulated by the action of general anesthetics. Despite a wide number of pharmacological studies, an extensive analysis of the cellular determinants of anesthesia at the microcircuits level is still missing. Here, by combining patch-clamp recordings and mathematical modeling, we examined the impact of sevoflurane, a general anesthetic widely employed in the clinical practice, on neuronal communication. The cerebellar microcircuit was used as a benchmark to analyze the action mechanisms of sevoflurane while a biologically realistic mathematical model was employed to explore at fine grain the molecular targets of anesthetic analyzing its impact on neuronal activity. The sevoflurane altered neurotransmission by strongly increasing GABAergic inhibition while decreasing glutamatergic NMDA activity. These changes caused a notable reduction of spike discharge in cerebellar granule cells (GrCs) following repetitive activation by excitatory mossy fibers (mfs). Unexpectedly, sevoflurane altered GrCs intrinsic excitability promoting action potential generation. Computational modelling revealed that this effect was triggered by an acceleration of persistent sodium current kinetics and by an increase in voltage dependent potassium current conductance. The overall effect was a reduced variability of GrCs responses elicited by mfs supporting the idea that sevoflurane shapes neuronal communication without silencing neural circuits.
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16
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Rizza MF, Locatelli F, Masoli S, Sánchez-Ponce D, Muñoz A, Prestori F, D'Angelo E. Stellate cell computational modeling predicts signal filtering in the molecular layer circuit of cerebellum. Sci Rep 2021; 11:3873. [PMID: 33594118 PMCID: PMC7886897 DOI: 10.1038/s41598-021-83209-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/17/2020] [Indexed: 12/22/2022] Open
Abstract
The functional properties of cerebellar stellate cells and the way they regulate molecular layer activity are still unclear. We have measured stellate cells electroresponsiveness and their activation by parallel fiber bursts. Stellate cells showed intrinsic pacemaking, along with characteristic responses to depolarization and hyperpolarization, and showed a marked short-term facilitation during repetitive parallel fiber transmission. Spikes were emitted after a lag and only at high frequency, making stellate cells to operate as delay-high-pass filters. A detailed computational model summarizing these physiological properties allowed to explore different functional configurations of the parallel fiber-stellate cell-Purkinje cell circuit. Simulations showed that, following parallel fiber stimulation, Purkinje cells almost linearly increased their response with input frequency, but such an increase was inhibited by stellate cells, which leveled the Purkinje cell gain curve to its 4 Hz value. When reciprocal inhibitory connections between stellate cells were activated, the control of stellate cells over Purkinje cell discharge was maintained only at very high frequencies. These simulations thus predict a new role for stellate cells, which could endow the molecular layer with low-pass and band-pass filtering properties regulating Purkinje cell gain and, along with this, also burst delay and the burst-pause responses pattern.
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Affiliation(s)
- Martina Francesca Rizza
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Francesca Locatelli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Diana Sánchez-Ponce
- Centro de Tecnología Biomédica (CTB), Technical University of Madrid, Madrid, Spain
| | - Alberto Muñoz
- Centro de Tecnología Biomédica (CTB), Technical University of Madrid, Madrid, Spain
- Departamento de Biología Celular, Complutense University of Madrid, Madrid, Spain
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy.
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.
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