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Stevenson M, Varghese R, Hebron ML, Liu X, Ratliff N, Smith A, Turner RS, Moussa C. Inhibition of discoidin domain receptor (DDR)-1 with nilotinib alters CSF miRNAs and is associated with reduced inflammation and vascular fibrosis in Alzheimer's disease. J Neuroinflammation 2023; 20:116. [PMID: 37194065 PMCID: PMC10186647 DOI: 10.1186/s12974-023-02802-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/10/2023] [Indexed: 05/18/2023] Open
Abstract
Discoidin Domain Receptor (DDR)-1 is activated by collagen. Nilotinib is a tyrosine kinase inhibitor that is FDA-approved for leukemia and potently inhibits DDR-1. Individuals diagnosed with mild-moderate Alzheimer's disease (AD) treated with nilotinib (versus placebo) for 12 months showed reduction of amyloid plaque and cerebrospinal fluid (CSF) amyloid, and attenuation of hippocampal volume loss. However, the mechanisms are unclear. Here, we explored unbiased next generation whole genome miRNA sequencing from AD patients CSF and miRNAs were matched with their corresponding mRNAs using gene ontology. Changes in CSF miRNAs were confirmed via measurement of CSF DDR1 activity and plasma levels of AD biomarkers. Approximately 1050 miRNAs are detected in the CSF but only 17 miRNAs are specifically altered between baseline and 12-month treatment with nilotinib versus placebo. Treatment with nilotinib significantly reduces collagen and DDR1 gene expression (upregulated in AD brain), in association with inhibition of CSF DDR1. Pro-inflammatory cytokines, including interleukins and chemokines are reduced along with caspase-3 gene expression. Specific genes that indicate vascular fibrosis, e.g., collagen, Transforming Growth Factors (TGFs) and Tissue Inhibitors of Metalloproteases (TIMPs) are altered by DDR1 inhibition with nilotinib. Specific changes in vesicular transport, including the neurotransmitters dopamine and acetylcholine, and autophagy genes, including ATGs, indicate facilitation of autophagic flux and cellular trafficking. Inhibition of DDR1 with nilotinib may be a safe and effective adjunct treatment strategy involving an oral drug that enters the CNS and adequately engages its target. DDR1 inhibition with nilotinib exhibits multi-modal effects not only on amyloid and tau clearance but also on anti-inflammatory markers that may reduce cerebrovascular fibrosis.
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Affiliation(s)
- Max Stevenson
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Rency Varghese
- Genomics and Epigenomics Shared Resource, Department of Oncology, Georgetown University Medical Center, Building D, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Michaeline L Hebron
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Xiaoguang Liu
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Nick Ratliff
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Amelia Smith
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - R Scott Turner
- Memory Disorders Program, Department of Neurology, Georgetown University Medical Center, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA
| | - Charbel Moussa
- Translational Neurotherapeutics Program, Laboratory for Dementia and Parkinsonism, Department of Neurology, Georgetown University Medical Center, Building D, Room 265, 4000 Reservoir Rd, NW, Washington, DC, 20057, USA.
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2
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Kasai H, Ucar H, Morimoto Y, Eto F, Okazaki H. Mechanical transmission at spine synapses: Short-term potentiation and working memory. Curr Opin Neurobiol 2023; 80:102706. [PMID: 36931116 DOI: 10.1016/j.conb.2023.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/17/2022] [Accepted: 02/15/2023] [Indexed: 03/17/2023]
Abstract
Do dendritic spines, which comprise the postsynaptic component of most excitatory synapses, exist only for their structural dynamics, receptor trafficking, and chemical and electrical compartmentation? The answer is no. Simultaneous investigation of both spine and presynaptic terminals has recently revealed a novel feature of spine synapses. Spine enlargement pushes the presynaptic terminals with muscle-like force and augments the evoked glutamate release for up to 20 min. We now summarize the evidence that such mechanical transmission shares critical features in common with short-term potentiation (STP) and may represent the cellular basis of short-term and working memory. Thus, spine synapses produce the force of learning to leave structural traces for both short and long-term memories.
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Affiliation(s)
- Haruo Kasai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
| | - Hasan Ucar
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yuichi Morimoto
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Fumihiro Eto
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hitoshi Okazaki
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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3
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KASAI H. Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2023; 99:254-305. [PMID: 37821392 PMCID: PMC10749395 DOI: 10.2183/pjab.99.018] [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: 05/13/2023] [Accepted: 07/11/2023] [Indexed: 10/13/2023]
Abstract
Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.
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Affiliation(s)
- Haruo KASAI
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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4
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Developmental depression-to-facilitation shift controls excitation-inhibition balance. Commun Biol 2022; 5:873. [PMID: 36008708 PMCID: PMC9411206 DOI: 10.1038/s42003-022-03801-2] [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: 02/15/2022] [Accepted: 08/04/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences. Using computational modelling this study proposes that the commonly observed depression-to-facilitation shift across development controls excitation-inhibition balance in the brain.
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5
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Mizusaki BEP, Li SSY, Costa RP, Sjöström PJ. Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning. PLoS Comput Biol 2022; 18:e1009409. [PMID: 35700188 PMCID: PMC9236267 DOI: 10.1371/journal.pcbi.1009409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 06/27/2022] [Accepted: 05/11/2022] [Indexed: 11/18/2022] Open
Abstract
A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions. Differences between functional properties of pre- or postsynaptically expressed long-term plasticity have not yet been explored in much detail. In this paper, we used minimalist models of STDP with different expression loci, in search of fundamental functional consequences. Biologically, presynaptic expression acts mostly on neurotransmitter release, thereby altering short-term synaptic dynamics, whereas postsynaptic expression affects mainly synaptic gain. We compared models where plasticity was expressed only presynaptically or postsynaptically, or in both ways. We found that postsynaptic plasticity had a bigger impact over response times, while both pre- and postsynaptic plasticity were similarly capable of detecting correlated inputs. A model with biologically tuned expression of plasticity achieved the same outcome over a range of frequencies. Also, postsynaptic spiking frequency was not directly affected by presynaptic plasticity of short-term plasticity alone, however in combination with a postsynaptic component, it helped restrain positive feedback, contributing to activity homeostasis. In conclusion, expression locus may determine affinity for distinct coding schemes while also contributing to keep activity within bounds. Our findings highlight the importance of carefully implementing expression of plasticity in biological modelling, since the locus of expression may affect functional outcomes in simulations.
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Affiliation(s)
- Beatriz Eymi Pimentel Mizusaki
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Programme, Departments of Medicine, Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - Sally Si Ying Li
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Programme, Departments of Medicine, Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
| | - Rui Ponte Costa
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
- Department of Physiology, University of Bern, Bern, Switzerland
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Per Jesper Sjöström
- Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Programme, Departments of Medicine, Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- * E-mail:
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6
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Chindemi G, Abdellah M, Amsalem O, Benavides-Piccione R, Delattre V, Doron M, Ecker A, Jaquier AT, King J, Kumbhar P, Monney C, Perin R, Rössert C, Tuncel AM, Van Geit W, DeFelipe J, Graupner M, Segev I, Markram H, Muller EB. A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex. Nat Commun 2022; 13:3038. [PMID: 35650191 PMCID: PMC9160074 DOI: 10.1038/s41467-022-30214-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 04/19/2022] [Indexed: 01/14/2023] Open
Abstract
Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.
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Affiliation(s)
- Giuseppe Chindemi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.
| | - Marwan Abdellah
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Oren Amsalem
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel.,Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Ruth Benavides-Piccione
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Vincent Delattre
- Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michael Doron
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - András Ecker
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Aurélien T Jaquier
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - James King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Caitlin Monney
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Christian Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Anil M Tuncel
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Javier DeFelipe
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Michael Graupner
- Université de Paris, SPPIN - Saints-Pères Paris Institute for the Neurosciences, CNRS, Paris, France
| | - Idan Segev
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland. .,Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada. .,CHU Sainte-Justine Research Center, Montréal, QC, Canada. .,Quebec Artificial Intelligence Institute (Mila), Montréal, Canada.
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7
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Schug S, Benzing F, Steger A. Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma. eLife 2021; 10:e69884. [PMID: 34661525 PMCID: PMC8716105 DOI: 10.7554/elife.69884] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/18/2021] [Indexed: 12/30/2022] Open
Abstract
When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.
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Affiliation(s)
- Simon Schug
- Institute of Neuroinformatics, University of Zurich & ETH ZurichZurichSwitzerland
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8
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NMDARs in granule cells contribute to parallel fiber-Purkinje cell synaptic plasticity and motor learning. Proc Natl Acad Sci U S A 2021; 118:2102635118. [PMID: 34507990 PMCID: PMC8449340 DOI: 10.1073/pnas.2102635118] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2021] [Indexed: 11/18/2022] Open
Abstract
Long-term synaptic plasticity is believed to be the cellular substrate of learning and memory. Synaptic plasticity rules are defined by the specific complement of receptors at the synapse and the associated downstream signaling mechanisms. In young rodents, at the cerebellar synapse between granule cells (GC) and Purkinje cells (PC), bidirectional plasticity is shaped by the balance between transcellular nitric oxide (NO) driven by presynaptic N-methyl-D-aspartate receptor (NMDAR) activation and postsynaptic calcium dynamics. However, the role and the location of NMDAR activation in these pathways is still debated in mature animals. Here, we show in adult rodents that NMDARs are present and functional in presynaptic terminals where their activation triggers NO signaling. In addition, we find that selective genetic deletion of presynaptic, but not postsynaptic, NMDARs prevents synaptic plasticity at parallel fiber-PC (PF-PC) synapses. Consistent with this finding, the selective deletion of GC NMDARs affects adaptation of the vestibulo-ocular reflex. Thus, NMDARs presynaptic to PCs are required for bidirectional synaptic plasticity and cerebellar motor learning.
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9
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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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10
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Rossbroich J, Trotter D, Beninger J, Tóth K, Naud R. Linear-nonlinear cascades capture synaptic dynamics. PLoS Comput Biol 2021; 17:e1008013. [PMID: 33720935 PMCID: PMC7993773 DOI: 10.1371/journal.pcbi.1008013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 03/25/2021] [Accepted: 02/25/2021] [Indexed: 11/18/2022] Open
Abstract
Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.
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Affiliation(s)
- Julian Rossbroich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Daniel Trotter
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
| | - John Beninger
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Katalin Tóth
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Richard Naud
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
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11
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Heterosynaptic cross-talk of pre- and postsynaptic strengths along segments of dendrites. Cell Rep 2021; 34:108693. [PMID: 33503435 DOI: 10.1016/j.celrep.2021.108693] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/13/2020] [Accepted: 01/05/2021] [Indexed: 11/20/2022] Open
Abstract
Dendrites are crucial for integrating incoming synaptic information. Individual dendritic branches are thought to constitute a signal processing unit, yet how neighboring synapses shape the boundaries of functional dendritic units is not well understood. Here, we address the cellular basis underlying the organization of the strengths of neighboring Schaffer collateral-CA1 synapses by optical quantal analysis and spine size measurements. Inducing potentiation at clusters of spines produces NMDA-receptor-dependent heterosynaptic plasticity. The direction of postsynaptic strength change shows distance dependency to the stimulated synapses where proximal synapses predominantly depress, whereas distal synapses potentiate; potentiation and depression are regulated by CaMKII and calcineurin, respectively. In contrast, heterosynaptic presynaptic plasticity is confined to weakening of presynaptic strength of nearby synapses, which requires CaMKII and the retrograde messenger nitric oxide. Our findings highlight the parallel engagement of multiple signaling pathways, each with characteristic spatial dynamics in shaping the local pattern of synaptic strengths.
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12
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Gontier C, Pfister JP. Identifiability of a Binomial Synapse. Front Comput Neurosci 2020; 14:558477. [PMID: 33117139 PMCID: PMC7561371 DOI: 10.3389/fncom.2020.558477] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 08/18/2020] [Indexed: 01/21/2023] Open
Abstract
Synapses are highly stochastic transmission units. A classical model describing this stochastic transmission is called the binomial model, and its underlying parameters can be estimated from postsynaptic responses to evoked stimuli. The accuracy of parameter estimates obtained via such a model-based approach depends on the identifiability of the model. A model is said to be structurally identifiable if its parameters can be uniquely inferred from the distribution of its outputs. However, this theoretical property does not necessarily imply practical identifiability. For instance, if the number of observations is low or if the recording noise is high, the model's parameters can only be loosely estimated. Structural identifiability, which is an intrinsic property of a model, has been widely characterized; but practical identifiability, which is a property of both the model and the experimental protocol, is usually only qualitatively assessed. Here, we propose a formal definition for the practical identifiability domain of a statistical model. For a given experimental protocol, this domain corresponds to the set of parameters for which the model is correctly identified as the ground truth compared to a simpler alternative model. Considering a model selection problem instead of a parameter inference problem allows to derive a non-arbitrary criterion for practical identifiability. We apply our definition to the study of neurotransmitter release at a chemical synapse. Our contribution to the analysis of synaptic stochasticity is three-fold: firstly, we propose a quantitative criterion for the practical identifiability of a statistical model, and compute the identifiability domains of different variants of the binomial release model (uni or multi-quantal, with or without short-term plasticity); secondly, we extend the Bayesian Information Criterion (BIC), a classically used tool for model selection, to models with correlated data (which is the case for most models of chemical synapses); finally, we show that our approach allows to perform data free model selection, i.e., to verify if a model used to fit data was indeed identifiable even without access to the data, but having only access to the fitted parameters.
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Affiliation(s)
- Camille Gontier
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Jean-Pascal Pfister
- Department of Physiology, University of Bern, Bern, Switzerland.,Institute of Neuroinformatics and Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
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13
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Local Design Principles at Hippocampal Synapses Revealed by an Energy-Information Trade-Off. eNeuro 2020; 7:ENEURO.0521-19.2020. [PMID: 32847867 PMCID: PMC7540928 DOI: 10.1523/eneuro.0521-19.2020] [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: 12/11/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 12/01/2022] Open
Abstract
Synapses across different brain regions display distinct structure-function relationships. We investigated the interplay of fundamental design constraints that shape the transmission properties of the excitatory CA3-CA1 pyramidal cell connection, a prototypic synapse for studying the mechanisms of learning in the mammalian hippocampus. This small synapse is characterized by probabilistic release of transmitter, which is markedly facilitated in response to naturally occurring trains of action potentials. Based on a physiologically motivated computational model of the rat CA3 presynaptic terminal, we show how unreliability and short-term dynamics of vesicular release work together to regulate the trade-off of information transfer versus energy use. We propose that individual CA3-CA1 synapses are designed to operate near the maximum possible capacity of information transmission in an efficient manner. Experimental measurements reveal a wide range of vesicular release probabilities at hippocampal synapses, which may be a necessary consequence of long-term plasticity and homeostatic mechanisms that manifest as presynaptic modifications of the release probability. We show that the timescales and magnitude of short-term plasticity (STP) render synaptic information transfer nearly independent of differences in release probability. Thus, individual synapses transmit optimally while maintaining a heterogeneous distribution of presynaptic strengths indicative of synaptically-encoded memory representations. Our results support the view that organizing principles that are evident on higher scales of neural organization percolate down to the design of an individual synapse.
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14
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Qu WR, Sun QH, Liu QQ, Jin HJ, Cui RJ, Yang W, Song DB, Li BJ. Role of CPEB3 protein in learning and memory: new insights from synaptic plasticity. Aging (Albany NY) 2020; 12:15169-15182. [PMID: 32619199 PMCID: PMC7425470 DOI: 10.18632/aging.103404] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/25/2020] [Indexed: 12/28/2022]
Abstract
The cytoplasmic polyadenylation element-binding (CPEB) protein family have demonstrated a crucial role for establishing synaptic plasticity and memory in model organisms. In this review, we outline evidence for CPEB3 as a crucial regulator of learning and memory, citing evidence from behavioral, electrophysiological and morphological studies. Subsequently, the regulatory role of CPEB3 is addressed in the context of the plasticity-related proteins, including AMPA and NMDA receptor subunits, actin, and the synaptic scaffolding protein PSD95. Finally, we delve into some of the more well-studied molecular mechanisms that guide the functionality of this dynamic regulator both during synaptic stimulation and in its basal state, including a variety of upstream regulators, post-translational modifications, and important structural domains that confer the unique properties of CPEB3. Collectively, this review offers a comprehensive view of the regulatory layers that allow a pathway for CPEB3’s maintenance of translational control that guides the necessary protein changes required for the establishment and maintenance of lasting synaptic plasticity and ultimately, long term learning and memory.
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Affiliation(s)
- Wen Rui Qu
- Department of Hand Surgery, The Second Hospital of Jilin University, Changchun, Jilin Province, China.,Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
| | - Qi Han Sun
- School of Pharmacy, Jilin University, Changchun, China
| | - Qian Qian Liu
- Department of Hand Surgery, The Second Hospital of Jilin University, Changchun, Jilin Province, China.,Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
| | - Hong Juan Jin
- Department of Plastic and Reconstructive Surgery, The First Hospital of Jilin University, Changchun, China
| | - Ran Ji Cui
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
| | - Wei Yang
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
| | - De Biao Song
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
| | - Bing Jin Li
- Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, China
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15
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Tong R, Emptage NJ, Padamsey Z. A two-compartment model of synaptic computation and plasticity. Mol Brain 2020; 13:79. [PMID: 32434549 PMCID: PMC7238589 DOI: 10.1186/s13041-020-00617-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/06/2020] [Indexed: 11/10/2022] Open
Abstract
The synapse is typically viewed as a single compartment, which acts as a linear gain controller on incoming input. Traditional plasticity rules enable this gain control to be dynamically optimized by Hebbian activity. Whilst this view nicely captures postsynaptic function, it neglects the non-linear dynamics of presynaptic function. Here we present a two-compartment model of the synapse in which the presynaptic terminal first acts to filter presynaptic input before the postsynaptic terminal, acting as a gain controller, amplifies or depresses transmission. We argue that both compartments are equipped with distinct plasticity rules to enable them to optimally adapt synaptic transmission to the statistics of pre- and postsynaptic activity. Specifically, we focus on how presynaptic plasticity enables presynaptic filtering to be optimally tuned to only transmit information relevant for postsynaptic firing. We end by discussing the advantages of having a presynaptic filter and propose future work to explore presynaptic function and plasticity in vivo.
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Affiliation(s)
- Rudi Tong
- Department of Pharmacology, University of Oxford, Mansfield Road, Oxford, OX1 3QT, UK. .,Current address: McGill University, Montreal Neurological Institute, 3801 University Street, Montreal, H3A 2B4, Canada.
| | - Nigel J Emptage
- Department of Pharmacology, University of Oxford, Mansfield Road, Oxford, OX1 3QT, UK.
| | - Zahid Padamsey
- Centre of Discovery Brain Sciences, University of Edinburgh, 9 George Square, Edinburgh, EH8 9XD, UK.
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16
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Surace SC, Pfister JP, Gerstner W, Brea J. On the choice of metric in gradient-based theories of brain function. PLoS Comput Biol 2020; 16:e1007640. [PMID: 32271761 PMCID: PMC7144966 DOI: 10.1371/journal.pcbi.1007640] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
This is a PLOS Computational Biology Education paper. The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to predict the dynamics of physiological quantities. In this context, steepest descent (also called gradient descent) is often suggested as an algorithmic principle of optimization potentially implemented by the brain. In practice, researchers often consider the vector of partial derivatives as the gradient. However, the definition of the gradient and the notion of a steepest direction depend on the choice of a metric. Because the choice of the metric involves a large number of degrees of freedom, the predictive power of models that are based on gradient descent must be called into question, unless there are strong constraints on the choice of the metric. Here, we provide a didactic review of the mathematics of gradient descent, illustrate common pitfalls of using gradient descent as a principle of brain function with examples from the literature, and propose ways forward to constrain the metric. A good skier may choose to follow the steepest direction to move as quickly as possible from the mountain peak to the base. Steepest descent in an abstract sense is also an appealing idea to describe adaptation and learning in the brain. For example, a scientist may hypothesize that synaptic or neuronal variables change in the direction of steepest descent in an abstract error landscape during learning of a new task or memorization of a new concept. There is, however, a pitfall in this reasoning: a multitude of steepest directions exists for any abstract error landscape because the steepest direction depends on how angles are measured, and it may be unclear how angles should be measured. Many scientists are taught that the steepest direction can be found by computing the vector of partial derivatives. But the vector of partial derivatives is equal to the steepest direction only if the angles in the abstract space are measured in a particular way. In this article, we provide a didactic review of the mathematics of finding steepest directions in abstract spaces, illustrate the pitfalls with examples from the neuroscience literature, and propose guidelines to constrain the way angles are measured in these spaces.
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Affiliation(s)
- Simone Carlo Surace
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroinformatics and Neuroscience Center Zurich, University Zurich and ETH Zurich, Zurich, Switzerland
| | - Jean-Pascal Pfister
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroinformatics and Neuroscience Center Zurich, University Zurich and ETH Zurich, Zurich, Switzerland
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Johanni Brea
- School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail:
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17
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Γ-Aminobutyric acid in adult brain: an update. Behav Brain Res 2019; 376:112224. [DOI: 10.1016/j.bbr.2019.112224] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 01/21/2023]
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18
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Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, Clopath C, Costa RP, de Berker A, Ganguli S, Gillon CJ, Hafner D, Kepecs A, Kriegeskorte N, Latham P, Lindsay GW, Miller KD, Naud R, Pack CC, Poirazi P, Roelfsema P, Sacramento J, Saxe A, Scellier B, Schapiro AC, Senn W, Wayne G, Yamins D, Zenke F, Zylberberg J, Therien D, Kording KP. A deep learning framework for neuroscience. Nat Neurosci 2019; 22:1761-1770. [PMID: 31659335 PMCID: PMC7115933 DOI: 10.1038/s41593-019-0520-2] [Citation(s) in RCA: 367] [Impact Index Per Article: 73.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 09/23/2019] [Indexed: 11/08/2022]
Abstract
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
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Affiliation(s)
- Blake A Richards
- Mila, Montréal, Quebec, Canada.
- School of Computer Science, McGill University, Montréal, Quebec, Canada.
- Department of Neurology & Neurosurgery, McGill University, Montréal, Quebec, Canada.
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
| | - Timothy P Lillicrap
- DeepMind, Inc., London, UK
- Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK
| | | | - Yoshua Bengio
- Mila, Montréal, Quebec, Canada
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
- Université de Montréal, Montréal, Quebec, Canada
| | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Amelia Christensen
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
| | - Rui Ponte Costa
- Computational Neuroscience Unit, School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, UK
- Department of Physiology, Universität Bern, Bern, Switzerland
| | | | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Google Brain, Mountain View, CA, USA
| | - Colleen J Gillon
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Danijar Hafner
- Google Brain, Mountain View, CA, USA
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Nikolaus Kriegeskorte
- Department of Psychology and Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA
| | - Peter Latham
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Grace W Lindsay
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Kenneth D Miller
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Richard Naud
- University of Ottawa Brain and Mind Institute, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Christopher C Pack
- Department of Neurology & Neurosurgery, McGill University, Montréal, Quebec, Canada
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Pieter Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - João Sacramento
- Institute of Neuroinformatics, ETH Zürich and University of Zürich, Zürich, Switzerland
| | - Andrew Saxe
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Benjamin Scellier
- Mila, Montréal, Quebec, Canada
- Université de Montréal, Montréal, Quebec, Canada
| | - Anna C Schapiro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter Senn
- Department of Physiology, Universität Bern, Bern, Switzerland
| | | | - Daniel Yamins
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Joel Zylberberg
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
- Department of Physics and Astronomy York University, Toronto, Ontario, Canada
- Center for Vision Research, York University, Toronto, Ontario, Canada
| | | | - Konrad P Kording
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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19
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Bykowska O, Gontier C, Sax AL, Jia DW, Montero ML, Bird AD, Houghton C, Pfister JP, Costa RP. Model-Based Inference of Synaptic Transmission. Front Synaptic Neurosci 2019; 11:21. [PMID: 31481887 PMCID: PMC6710341 DOI: 10.3389/fnsyn.2019.00021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/29/2019] [Indexed: 12/15/2022] Open
Abstract
Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.
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Affiliation(s)
- Ola Bykowska
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - Camille Gontier
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Anne-Lene Sax
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - David W. Jia
- Department of Physiology, Anatomy and Genetics, Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Milton Llera Montero
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
- School of Psychological Science, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - Alex D. Bird
- Ernst Strungmann Institute for Neuroscience in Cooperation With Max Planck Society, Frankfurt, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Conor Houghton
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - Jean-Pascal Pfister
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroinformatics and Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Rui Ponte Costa
- Computational Neuroscience Unit, Department of Computer Science, SCEEM, Faculty of Engineering, University of Bristol, Bristol, United Kingdom
- Department of Physiology, University of Bern, Bern, Switzerland
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20
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Letellier M, Levet F, Thoumine O, Goda Y. Differential role of pre- and postsynaptic neurons in the activity-dependent control of synaptic strengths across dendrites. PLoS Biol 2019; 17:e2006223. [PMID: 31166943 PMCID: PMC6576792 DOI: 10.1371/journal.pbio.2006223] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 06/17/2019] [Accepted: 05/17/2019] [Indexed: 01/07/2023] Open
Abstract
Neurons receive a large number of active synaptic inputs from their many presynaptic partners across their dendritic tree. However, little is known about how the strengths of individual synapses are controlled in balance with other synapses to effectively encode information while maintaining network homeostasis. This is in part due to the difficulty in assessing the activity of individual synapses with identified afferent and efferent connections for a synapse population in the brain. Here, to gain insights into the basic cellular rules that drive the activity-dependent spatial distribution of pre- and postsynaptic strengths across incoming axons and dendrites, we combine patch-clamp recordings with live-cell imaging of hippocampal pyramidal neurons in dissociated cultures and organotypic slices. Under basal conditions, both pre- and postsynaptic strengths cluster on single dendritic branches according to the identity of the presynaptic neurons, thus highlighting the ability of single dendritic branches to exhibit input specificity. Stimulating a single presynaptic neuron induces input-specific and dendritic branchwise spatial clustering of presynaptic strengths, which accompanies a widespread multiplicative scaling of postsynaptic strengths in dissociated cultures and heterosynaptic plasticity at distant synapses in organotypic slices. Our study provides evidence for a potential homeostatic mechanism by which the rapid changes in global or distant postsynaptic strengths compensate for input-specific presynaptic plasticity.
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Affiliation(s)
- Mathieu Letellier
- RIKEN Brain Science Institute, Wako, Saitama, Japan
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, France
- Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, France
- * E-mail: (ML); (YG)
| | - Florian Levet
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, France
- Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, France
- Bordeaux Imaging Center, University of Bordeaux, Bordeaux, France
- Bordeaux Imaging Center, CNRS UMS 3420, Bordeaux, France
- Bordeaux Imaging Center, INSERM US04, Bordeaux, France
| | - Olivier Thoumine
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, France
- Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, France
| | - Yukiko Goda
- RIKEN Center for Brain Science, Wako, Saitama, Japan
- * E-mail: (ML); (YG)
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21
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Pena-Bravo JI, Penrod R, Reichel CM, Lavin A. Methamphetamine Self-Administration Elicits Sex-Related Changes in Postsynaptic Glutamate Transmission in the Prefrontal Cortex. eNeuro 2019; 6:ENEURO.0401-18.2018. [PMID: 30693312 PMCID: PMC6348447 DOI: 10.1523/eneuro.0401-18.2018] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 12/17/2018] [Accepted: 12/20/2018] [Indexed: 12/11/2022] Open
Abstract
Preclinical and clinical research has shown that females are more vulnerable to the rewarding effects of stimulants, and it has been proposed that estrogens may play a role in this enhanced sensitivity; however sex differences in methamphetamine (METH)-induced neuroplasticity have not been explored. To address this gap in knowledge, we recorded from the prelimbic area of the prefrontal cortex (PL-PFC) of male and female rats following long access METH self-administration (SA) and investigated the resulting long-term synaptic neuroadaptations. Males and females took similar amounts of METH during SA; however, female rats exhibit significant synaptic baseline differences when compared to males. Furthermore, females exhibited a significant increase in evoked excitatory currents. This increase in evoked glutamate was correlated with increases in NMDA currents and was not affected by application of a GluN2B selective blocker. We propose that METH SA selectively upregulates GluN2B-lacking NMDA receptors (NMDAR) in the PFC of female rats. Our results may provide a mechanistic explanation for the sex differences reported for METH addiction in females.
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Affiliation(s)
| | - Rachel Penrod
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425
| | - Carmela M. Reichel
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425
| | - Antonieta Lavin
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425
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22
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Llera-Montero M, Sacramento J, Costa RP. Computational roles of plastic probabilistic synapses. Curr Opin Neurobiol 2018; 54:90-97. [PMID: 30308457 DOI: 10.1016/j.conb.2018.09.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/02/2018] [Accepted: 09/06/2018] [Indexed: 11/18/2022]
Abstract
The probabilistic nature of synaptic transmission has remained enigmatic. However, recent developments have started to shed light on why the brain may rely on probabilistic synapses. Here, we start out by reviewing experimental evidence on the specificity and plasticity of synaptic response statistics. Next, we overview different computational perspectives on the function of plastic probabilistic synapses for constrained, statistical and deep learning. We highlight that all of these views require some form of optimisation of probabilistic synapses, which has recently gained support from theoretical analysis of long-term synaptic plasticity experiments. Finally, we contrast these different computational views and propose avenues for future research. Overall, we argue that the time is ripe for a better understanding of the computational functions of probabilistic synapses.
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Affiliation(s)
- Milton Llera-Montero
- Computational Neuroscience Unit, Department of Computer Science, School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, United Kingdom; Bristol Neuroscience, University of Bristol, United Kingdom; School of Psychological Science, Faculty of Life Sciences, University of Bristol, United Kingdom
| | | | - Rui Ponte Costa
- Computational Neuroscience Unit, Department of Computer Science, School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, United Kingdom; Bristol Neuroscience, University of Bristol, United Kingdom; Department of Physiology, University of Bern, Switzerland; Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom.
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23
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Redundancy in synaptic connections enables neurons to learn optimally. Proc Natl Acad Sci U S A 2018; 115:E6871-E6879. [PMID: 29967182 DOI: 10.1073/pnas.1803274115] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring.
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24
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…with Love, from Post to Pre. Neuron 2017; 96:9-10. [PMID: 28957680 DOI: 10.1016/j.neuron.2017.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In this issue of Neuron, Costa et al. (2017) introduce a theoretical framework that predicts the ratio of presynaptic and postsynaptic changes taking place during LTP and LTD and show that these processes co-operate so as to optimize the postsynaptic response statistics.
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