1
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Karpova A, Aly AAA, Marosi EL, Mikulovic S. Fiber-based in vivo imaging: unveiling avenues for exploring mechanisms of synaptic plasticity and neuronal adaptations underlying behavior. NEUROPHOTONICS 2024; 11:S11507. [PMID: 38390518 PMCID: PMC10883581 DOI: 10.1117/1.nph.11.s1.s11507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024]
Abstract
In recent decades, various subfields within neuroscience, spanning molecular, cellular, and systemic dimensions, have significantly advanced our understanding of the elaborate molecular and cellular mechanisms that underpin learning, memory, and adaptive behaviors. There have been notable advancements in imaging techniques, particularly in reaching superficial brain structures. This progress has led to their widespread adoption in numerous laboratories. However, essential physiological and cognitive processes, including sensory integration, emotional modulation of motivated behavior, motor regulation, learning, and memory consolidation, are intricately encoded within deeper brain structures. Hence, visualization techniques such as calcium imaging using miniscopes have gained popularity for studying brain activity in unrestrained animals. Despite its utility, miniscope technology is associated with substantial brain tissue damage caused by gradient refractive index lens implantation. Furthermore, its imaging capabilities are primarily confined to the neuronal somata level, thus constraining a comprehensive exploration of subcellular processes underlying adaptive behaviors. Consequently, the trajectory of neuroscience's future hinges on the development of minimally invasive optical fiber-based endo-microscopes optimized for cellular, subcellular, and molecular imaging within the intricate depths of the brain. In pursuit of this goal, select research groups have invested significant efforts in advancing this technology. In this review, we present a perspective on the potential impact of this innovation on various aspects of neuroscience, enabling the functional exploration of in vivo cellular and subcellular processes that underlie synaptic plasticity and the neuronal adaptations that govern behavior.
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Affiliation(s)
- Anna Karpova
- Leibniz Institute for Neurobiology, RG Neuroplasticity, Magdeburg, Germany
- Otto von Guericke University, Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Ahmed A A Aly
- Leibniz Institute for Neurobiology, RG Neuroplasticity, Magdeburg, Germany
| | - Endre Levente Marosi
- Leibniz Institute for Neurobiology, RG Cognition and Emotion, Magdeburg, Germany
| | - Sanja Mikulovic
- Otto von Guericke University, Center for Behavioral Brain Sciences, Magdeburg, Germany
- Leibniz Institute for Neurobiology, RG Cognition and Emotion, Magdeburg, Germany
- German Centre for Mental Health (DZPG), Magdeburg, Germany
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2
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Harris KM, Kuwajima M, Flores JC, Zito K. Synapse-specific structural plasticity that protects and refines local circuits during LTP and LTD. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230224. [PMID: 38853547 DOI: 10.1098/rstb.2023.0224] [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: 10/17/2023] [Accepted: 01/05/2024] [Indexed: 06/11/2024] Open
Abstract
Synapses form trillions of connections in the brain. Long-term potentiation (LTP) and long-term depression (LTD) are cellular mechanisms vital for learning that modify the strength and structure of synapses. Three-dimensional reconstruction from serial section electron microscopy reveals three distinct pre- to post-synaptic arrangements: strong active zones (AZs) with tightly docked vesicles, weak AZs with loose or non-docked vesicles, and nascent zones (NZs) with a postsynaptic density but no presynaptic vesicles. Importantly, LTP can be temporarily saturated preventing further increases in synaptic strength. At the onset of LTP, vesicles are recruited to NZs, converting them to AZs. During recovery of LTP from saturation (1-4 h), new NZs form, especially on spines where AZs are most enlarged by LTP. Sentinel spines contain smooth endoplasmic reticulum (SER), have the largest synapses and form clusters with smaller spines lacking SER after LTP recovers. We propose a model whereby NZ plasticity provides synapse-specific AZ expansion during LTP and loss of weak AZs that drive synapse shrinkage during LTD. Spine clusters become functionally engaged during LTP or disassembled during LTD. Saturation of LTP or LTD probably acts to protect recently formed memories from ongoing plasticity and may account for the advantage of spaced over massed learning. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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Affiliation(s)
- Kristen M Harris
- Department of Neuroscience and Center for Learning and Memory, The University of Texas at Austin , Austin, TX 78712, USA
| | - Masaaki Kuwajima
- Department of Neuroscience and Center for Learning and Memory, The University of Texas at Austin , Austin, TX 78712, USA
| | - Juan C Flores
- Center for Neuroscience, University of California , Davis, CA 95618, USA
| | - Karen Zito
- Center for Neuroscience, University of California , Davis, CA 95618, USA
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3
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Turegano-Lopez M, de Las Pozas F, Santuy A, Rodriguez JR, DeFelipe J, Merchan-Perez A. Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity. Commun Biol 2024; 7:796. [PMID: 38951162 PMCID: PMC11217374 DOI: 10.1038/s42003-024-06491-0] [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: 12/18/2023] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified "skeleton" of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.
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Affiliation(s)
- Marta Turegano-Lopez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
| | - Felix de Las Pozas
- Visualization & Graphics Lab (VG-Lab), Universidad Rey Juan Carlos, C/Tulipán S/N, Móstoles, 28933, Madrid, Spain
| | - Andrea Santuy
- Department of Basic Sciences, Universitat Internacional de Catalunya (UIC), San Cugat del Vallès, 08195, Barcelona, Spain
| | - Jose-Rodrigo Rodriguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37, 28002, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37, 28002, Madrid, Spain
| | - Angel Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28031, Madrid, Spain.
- Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain.
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4
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Musgrove MRB, Mikhaylova M, Bredy TW. Fundamental Neurochemistry Review: At the intersection between the brain and the immune system: Non-coding RNAs spanning learning, memory and adaptive immunity. J Neurochem 2024; 168:961-976. [PMID: 38339812 DOI: 10.1111/jnc.16071] [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: 10/30/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Non-coding RNAs (ncRNAs) are highly plastic RNA molecules that can sequester cellular proteins and other RNAs, serve as transporters of cellular cargo and provide spatiotemporal feedback to the genome. Mounting evidence indicates that ncRNAs are central to biology, and are critical for neuronal development, metabolism and intra- and intercellular communication in the brain. Their plasticity arises from state-dependent dynamic structure states that can be influenced by cell type and subcellular environment, which can subsequently enable the same ncRNA with discrete functions in different contexts. Here, we highlight different classes of brain-enriched ncRNAs, including microRNA, long non-coding RNA and other enigmatic ncRNAs, that are functionally important for both learning and memory and adaptive immunity, and describe how they may promote cross-talk between these two evolutionarily ancient biological systems.
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Affiliation(s)
- Mason R B Musgrove
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Marina Mikhaylova
- AG Optobiologie, Institute für Biologie, Humboldt Universität zu Berlin, Berlin, Germany
| | - Timothy W Bredy
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
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5
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Choucry A, Nomoto M, Inokuchi K. Engram mechanisms of memory linking and identity. Nat Rev Neurosci 2024; 25:375-392. [PMID: 38664582 DOI: 10.1038/s41583-024-00814-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2024] [Indexed: 05/25/2024]
Abstract
Memories are thought to be stored in neuronal ensembles referred to as engrams. Studies have suggested that when two memories occur in quick succession, a proportion of their engrams overlap and the memories become linked (in a process known as prospective linking) while maintaining their individual identities. In this Review, we summarize the key principles of memory linking through engram overlap, as revealed by experimental and modelling studies. We describe evidence of the involvement of synaptic memory substrates, spine clustering and non-linear neuronal capacities in prospective linking, and suggest a dynamic somato-synaptic model, in which memories are shared between neurons yet remain separable through distinct dendritic and synaptic allocation patterns. We also bring into focus retrospective linking, in which memories become associated after encoding via offline reactivation, and discuss key temporal and mechanistic differences between prospective and retrospective linking, as well as the potential differences in their cognitive outcomes.
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Affiliation(s)
- Ali Choucry
- Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Masanori Nomoto
- Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
- CREST, Japan Science and Technology Agency (JST), University of Toyama, Toyama, Japan
- Japan Agency for Medical Research and Development (AMED), Tokyo, Japan
| | - Kaoru Inokuchi
- Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan.
- CREST, Japan Science and Technology Agency (JST), University of Toyama, Toyama, Japan.
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6
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Noguchi J, Watanabe S, Oga T, Isoda R, Nakagaki K, Sakai K, Sumida K, Hoshino K, Saito K, Miyawaki I, Sugano E, Tomita H, Mizukami H, Watakabe A, Yamamori T, Ichinohe N. Altered projection-specific synaptic remodeling and its modification by oxytocin in an idiopathic autism marmoset model. Commun Biol 2024; 7:642. [PMID: 38802535 PMCID: PMC11130163 DOI: 10.1038/s42003-024-06345-9] [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: 10/22/2023] [Accepted: 05/16/2024] [Indexed: 05/29/2024] Open
Abstract
Alterations in the experience-dependent and autonomous elaboration of neural circuits are assumed to underlie autism spectrum disorder (ASD), though it is unclear what synaptic traits are responsible. Here, utilizing a valproic acid-induced ASD marmoset model, which shares common molecular features with idiopathic ASD, we investigate changes in the structural dynamics of tuft dendrites of upper-layer pyramidal neurons and adjacent axons in the dorsomedial prefrontal cortex through two-photon microscopy. In model marmosets, dendritic spine turnover is upregulated, and spines are generated in clusters and survived more often than in control marmosets. Presynaptic boutons in local axons, but not in commissural long-range axons, demonstrate hyperdynamic turnover in model marmosets, suggesting alterations in projection-specific plasticity. Intriguingly, nasal oxytocin administration attenuates clustered spine emergence in model marmosets. Enhanced clustered spine generation, possibly unique to certain presynaptic partners, may be associated with ASD and be a potential therapeutic target.
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Affiliation(s)
- Jun Noguchi
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan.
| | - Satoshi Watanabe
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Tomofumi Oga
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Risa Isoda
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Keiko Nakagaki
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Kazuhisa Sakai
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Kayo Sumida
- Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Osaka, Japan
| | - Kohei Hoshino
- Preclinical Research Laboratories, Sumitomo Pharma Co., Ltd., Osaka, Japan
| | - Koichi Saito
- Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Osaka, Japan
| | - Izuru Miyawaki
- Preclinical Research Laboratories, Sumitomo Pharma Co., Ltd., Osaka, Japan
| | - Eriko Sugano
- Laboratory of Visual Neuroscience, Graduate Course in Biological Sciences, Iwate University, Morioka, Japan
| | - Hiroshi Tomita
- Laboratory of Visual Neuroscience, Graduate Course in Biological Sciences, Iwate University, Morioka, Japan
| | - Hiroaki Mizukami
- Division of Genetic Therapeutics, Jichi Medical University, Shimotsuke, Japan
| | - Akiya Watakabe
- Laboratory for Molecular Analysis of Higher Brain Function, Center for Brain Science, RIKEN, Wako, Japan
| | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, Center for Brain Science, RIKEN, Wako, Japan
- Laboratory for Haptic Perception and Cognitive Physiology, Center for Brain Science, RIKEN, Wako, Japan
- Department of Marmoset Biology and Medicine, CIEM, Kawasaki, Japan
| | - Noritaka Ichinohe
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan.
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7
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Moreno-Sanchez A, Vasserman AN, Jang H, Hina BW, von Reyn CR, Ausborn J. Morphology and synapse topography optimize linear encoding of synapse numbers in Drosophila looming responsive descending neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591016. [PMID: 38712267 PMCID: PMC11071487 DOI: 10.1101/2024.04.24.591016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Synapses are often precisely organized on dendritic arbors, yet the role of synaptic topography in dendritic integration remains poorly understood. Utilizing electron microscopy (EM) connectomics we investigate synaptic topography in Drosophila melanogaster looming circuits, focusing on retinotopically tuned visual projection neurons (VPNs) that synapse onto descending neurons (DNs). Synapses of a given VPN type project to non-overlapping regions on DN dendrites. Within these spatially constrained clusters, synapses are not retinotopically organized, but instead adopt near random distributions. To investigate how this organization strategy impacts DN integration, we developed multicompartment models of DNs fitted to experimental data and using precise EM morphologies and synapse locations. We find that DN dendrite morphologies normalize EPSP amplitudes of individual synaptic inputs and that near random distributions of synapses ensure linear encoding of synapse numbers from individual VPNs. These findings illuminate how synaptic topography influences dendritic integration and suggest that linear encoding of synapse numbers may be a default strategy established through connectivity and passive neuron properties, upon which active properties and plasticity can then tune as needed.
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Affiliation(s)
- Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - Alexander N. Vasserman
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - HyoJong Jang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Catherine R. von Reyn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
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8
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Banazadeh M, Abiri A, Poortaheri MM, Asnaashari L, Langarizadeh MA, Forootanfar H. Unexplored power of CRISPR-Cas9 in neuroscience, a multi-OMICs review. Int J Biol Macromol 2024; 263:130413. [PMID: 38408576 DOI: 10.1016/j.ijbiomac.2024.130413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/27/2023] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
The neuroscience and neurobiology of gene editing to enhance learning and memory is of paramount interest to the scientific community. The advancements of CRISPR system have created avenues to treat neurological disorders by means of versatile modalities varying from expression to suppression of genes and proteins. Neurodegenerative disorders have also been attributed to non-canonical DNA secondary structures by affecting neuron activity through controlling gene expression, nucleosome shape, transcription, translation, replication, and recombination. Changing DNA regulatory elements which could contribute to the fate and function of neurons are thoroughly discussed in this review. This study presents the ability of CRISPR system to boost learning power and memory, treat or cure genetically-based neurological disorders, and alleviate psychiatric diseases by altering the activity and the irritability of the neurons at the synaptic cleft through DNA manipulation, and also, epigenetic modifications using Cas9. We explore and examine how each different OMIC techniques can come useful when altering DNA sequences. Such insight into the underlying relationship between OMICs and cellular behaviors leads us to better neurological and psychiatric therapeutics by intelligently designing and utilizing the CRISPR/Cas9 technology.
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Affiliation(s)
- Mohammad Banazadeh
- Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Ardavan Abiri
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT 06520, USA
| | | | - Lida Asnaashari
- Student Research Committee, Kerman Universiy of Medical Sciences, Kerman, Iran
| | - Mohammad Amin Langarizadeh
- Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
| | - Hamid Forootanfar
- Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran.
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9
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Stevens NA, Lankisch K, Draguhn A, Engelhardt M, Both M, Thome C. Increased Interhemispheric Connectivity of a Distinct Type of Hippocampal Pyramidal Cells. J Neurosci 2024; 44:e0440232023. [PMID: 38123997 PMCID: PMC10869156 DOI: 10.1523/jneurosci.0440-23.2023] [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: 02/28/2023] [Revised: 11/02/2023] [Accepted: 11/19/2023] [Indexed: 12/23/2023] Open
Abstract
Neurons typically generate action potentials at their axon initial segment based on the integration of synaptic inputs. In many neurons, the axon extends from the soma, equally weighting dendritic inputs. A notable exception is found in a subset of hippocampal pyramidal cells where the axon emerges from a basal dendrite. This structure allows these axon-carrying dendrites (AcDs) a privileged input route. We found that in male mice, such cells in the CA1 region receive stronger excitatory input from the contralateral CA3, compared with those with somatic axon origins. This is supported by a higher count of putative synapses from contralateral CA3 on the AcD. These findings, combined with prior observations of their distinct role in sharp-wave ripple firing, suggest a key role of this neuron subset in coordinating bi-hemispheric hippocampal activity during memory-centric oscillations.
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Affiliation(s)
- Nikolas Andreas Stevens
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Katja Lankisch
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Andreas Draguhn
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Maren Engelhardt
- Institute of Anatomy and Cell Biology, Medical Faculty, Johannes Kepler University Linz, 4020 Linz, Austria
| | - Martin Both
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Christian Thome
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
- Institute of Anatomy and Cell Biology, Medical Faculty, Johannes Kepler University Linz, 4020 Linz, Austria
- Institute of Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California 94305
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10
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Atta-Ur-Rahman. Protein Folding and Molecular Basis of Memory: Molecular Vibrations and Quantum Entanglement as Basis of Consciousness. Curr Med Chem 2024; 31:258-265. [PMID: 37424348 DOI: 10.2174/0929867331666230707123345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/11/2023]
Affiliation(s)
- Atta-Ur-Rahman
- Kings College, University of Cambridge, Cambridge CB2 1st, United Kingdom
- H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
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11
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Makarov R, Pagkalos M, Poirazi P. Dendrites and efficiency: Optimizing performance and resource utilization. Curr Opin Neurobiol 2023; 83:102812. [PMID: 37980803 DOI: 10.1016/j.conb.2023.102812] [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: 05/31/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/21/2023]
Abstract
The brain is a highly efficient system that has evolved to optimize performance under limited resources. In this review, we highlight recent theoretical and experimental studies that support the view that dendrites make information processing and storage in the brain more efficient. This is achieved through the dynamic modulation of integration versus segregation of inputs and activity within a neuron. We argue that under conditions of limited energy and space, dendrites help biological networks to implement complex functions such as processing natural stimuli on behavioral timescales, performing the inference process on those stimuli in a context-specific manner, and storing the information in overlapping populations of neurons. A global picture starts to emerge, in which dendrites help the brain achieve efficiency through a combination of optimization strategies that balance the tradeoff between performance and resource utilization.
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Affiliation(s)
- Roman Makarov
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/_RomanMakarov
| | - Michalis Pagkalos
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/MPagkalos
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece.
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12
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Fitch WT. Cellular computation and cognition. Front Comput Neurosci 2023; 17:1107876. [PMID: 38077750 PMCID: PMC10702520 DOI: 10.3389/fncom.2023.1107876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 10/09/2023] [Indexed: 05/28/2024] Open
Abstract
Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of synaptic inputs computed by the "units" in standard neural network models. Neurons are eukaryotic cells that store information not only in synapses, but also in their dendritic structure and connectivity, as well as genetic "marking" in the epigenome of each individual cell. Each neuron computes a complex nonlinear function of its inputs, roughly equivalent in processing capacity to an entire 1990s-era neural network model. Furthermore, individual cells provide the biological interface between gene expression, ongoing neural processing, and stored long-term memory traces. Neurons in all organisms have these properties, which are thus relevant to all of neuroscience and cognitive biology. Single-cell computation may also play a particular role in explaining some unusual features of human cognition. The recognition of the centrality of cellular computation to "natural computation" in brains, and of the constraints it imposes upon brain evolution, thus has important implications for the evolution of cognition, and how we study it.
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Affiliation(s)
- W. Tecumseh Fitch
- Faculty of Life Sciences and Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
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13
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Kastellakis G, Tasciotti S, Pandi I, Poirazi P. The dendritic engram. Front Behav Neurosci 2023; 17:1212139. [PMID: 37576932 PMCID: PMC10412934 DOI: 10.3389/fnbeh.2023.1212139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions.
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Affiliation(s)
- George Kastellakis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion, Greece
| | - Simone Tasciotti
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion, Greece
- Department of Biology, University of Crete, Heraklion, Greece
| | - Ioanna Pandi
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion, Greece
- Department of Biology, University of Crete, Heraklion, Greece
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion, Greece
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14
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Das S, Lituma PJ, Castillo PE, Singer RH. Maintenance of a short-lived protein required for long-term memory involves cycles of transcription and local translation. Neuron 2023; 111:2051-2064.e6. [PMID: 37100055 PMCID: PMC10330212 DOI: 10.1016/j.neuron.2023.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 01/03/2023] [Accepted: 04/03/2023] [Indexed: 04/28/2023]
Abstract
Activity-dependent expression of immediate early genes (IEGs) is critical for long-term synaptic remodeling and memory. It remains unknown how IEGs are maintained for memory despite rapid transcript and protein turnover. To address this conundrum, we monitored Arc, an IEG essential for memory consolidation. Using a knockin mouse where endogenous Arc alleles were fluorescently tagged, we performed real-time imaging of Arc mRNA dynamics in individual neurons in cultures and brain tissue. Unexpectedly, a single burst stimulation was sufficient to induce cycles of transcriptional reactivation in the same neuron. Subsequent transcription cycles required translation, whereby new Arc proteins engaged in autoregulatory positive feedback to reinduce transcription. The ensuing Arc mRNAs preferentially localized at sites marked by previous Arc protein, assembling a "hotspot" of translation, and consolidating "hubs" of dendritic Arc. These cycles of transcription-translation coupling sustain protein expression and provide a mechanism by which a short-lived event may support long-term memory.
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Affiliation(s)
- Sulagna Das
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Program in RNA Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA.
| | - Pablo J Lituma
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Pablo E Castillo
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Robert H Singer
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Program in RNA Biology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA; Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA.
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15
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Moldwin T, Kalmenson M, Segev I. Asymmetric Voltage Attenuation in Dendrites Can Enable Hierarchical Heterosynaptic Plasticity. eNeuro 2023; 10:ENEURO.0014-23.2023. [PMID: 37414554 PMCID: PMC10354808 DOI: 10.1523/eneuro.0014-23.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 05/16/2023] [Accepted: 06/14/2023] [Indexed: 07/08/2023] Open
Abstract
Long-term synaptic plasticity is mediated via cytosolic calcium concentrations ([Ca2+]). Using a synaptic model that implements calcium-based long-term plasticity via two sources of Ca2+ - NMDA receptors and voltage-gated calcium channels (VGCCs) - we show in dendritic cable simulations that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at nonactivated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry can produce a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at the active synapses, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity.
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Affiliation(s)
| | - Menachem Kalmenson
- Department of Neurobiology, The Hebrew University of Jerusalem, 91904 Jerusalem, Israel
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences
- Department of Neurobiology, The Hebrew University of Jerusalem, 91904 Jerusalem, Israel
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16
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Zhuravlev AV. Three levels of information processing in the brain. Biosystems 2023:104934. [PMID: 37245794 DOI: 10.1016/j.biosystems.2023.104934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 05/30/2023]
Abstract
Information, the measure of order in a complex system, is the opposite of entropy, the measure of chaos and disorder. We can distinguish several levels at which information is processed in the brain. The first one is the level of serial molecular genetic processes, similar in some aspects to digital computations (DC). At the same time, higher cognitive activity is probably based on parallel neural network computations (NNC). The advantage of neural networks is their intrinsic ability to learn, adapting their parameters to specific tasks and to external data. However, there seems to be a third level of information processing as well, which involves subjective consciousness and its units, so called qualia. They are difficult to study experimentally, and the very fact of their existence is hard to explain within the framework of modern physical theory. Here I propose a way to consider consciousness as the extension of basic physical laws - namely, total entropy dissipation leading to a system simplification. At the level of subjective consciousness, the brain seems to convert information embodied by neural activity to a more simple and compact form, internally observed as qualia. Whereas physical implementations of both DC and NNC are essentially approximate and probabilistic, qualia-associated computations (QAC) make the brain capable of recognizing general laws and relationships. While elaborating a behavioral program, the conscious brain does not act blindly or gropingly but according to the very meaning of such general laws, which gives it an advantage compared to any artificial intelligence system.
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Affiliation(s)
- Aleksandr V Zhuravlev
- I. P. Pavlov Institute of Physiology, nab Makarova 6, 199034, St Petersburg, Russian Federation.
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17
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Buchholz MO, Gastone Guilabert A, Ehret B, Schuhknecht GFP. How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output. PLoS Comput Biol 2023; 19:e1011046. [PMID: 37068099 PMCID: PMC10153727 DOI: 10.1371/journal.pcbi.1011046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/02/2023] [Accepted: 03/24/2023] [Indexed: 04/18/2023] Open
Abstract
Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.
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Affiliation(s)
- Moritz O Buchholz
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | | | - Benjamin Ehret
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | - Gregor F P Schuhknecht
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
- Department of Molecular and Cellular Biology, Harvard University Cambridge, Massachusetts, United States of America
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18
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Liu M, Sun X. Spatial integration of dendrites in fast-spiking basket cells. Front Neurosci 2023; 17:1132980. [PMID: 37081933 PMCID: PMC10110864 DOI: 10.3389/fnins.2023.1132980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Dendrites of fast-spiking basket cells (FS BCs) impact neural circuit functions in brain with both supralinear and sublinear integration strategies. Diverse spatial synaptic inputs and active properties of dendrites lead to distinct neuronal firing patterns. How the FS BCs with this bi-modal dendritic integration respond to different spatial dispersion of synaptic inputs remains unclear. In this study, we construct a multi-compartmental model of FS BC and analyze neuronal firings following simulated synaptic protocols from fully clustered to fully dispersed. Under these stimulation protocols, we find that supralinear dendrites dominate somatic firing of FS BC, while the preference for dispersing is due to sublinear dendrites. Moreover, we find that dendritic diameter and Ca2+-permeable AMPA conductance play an important role in it, while A-type K+ channel and NMDA conductance have little effect. The obtained results may give some implications for understanding dendritic computation.
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19
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Galindo SE, Wood AJ, Cooney PC, Hammond LA, Grueber WB. Axon-axon interactions determine modality-specific wiring and subcellular synaptic specificity in a somatosensory circuit. Development 2023; 150:dev199832. [PMID: 36920224 PMCID: PMC10112896 DOI: 10.1242/dev.199832] [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: 05/25/2021] [Accepted: 02/09/2023] [Indexed: 03/16/2023]
Abstract
Synaptic connections between neurons are often formed in precise subcellular regions of dendritic arbors with implications for information processing within neurons. Cell-cell interactions are widely important for circuit wiring; however, their role in subcellular specificity is not well understood. We studied the role of axon-axon interactions in precise targeting and subcellular wiring of Drosophila somatosensory circuitry. Axons of nociceptive and gentle touch neurons terminate in adjacent, non-overlapping layers in the central nervous system (CNS). Nociceptor and touch receptor axons synapse onto distinct dendritic regions of a second-order interneuron, the dendrites of which span these layers, forming touch-specific and nociceptive-specific connectivity. We found that nociceptor ablation elicited extension of touch receptor axons and presynapses into the nociceptor recipient region, supporting a role for axon-axon interactions in somatosensory wiring. Conversely, touch receptor ablation did not lead to expansion of nociceptor axons, consistent with unidirectional axon-axon interactions. Live imaging provided evidence for sequential arborization of nociceptive and touch neuron axons in the CNS. We propose that axon-axon interactions and modality-specific timing of axon targeting play key roles in subcellular connection specificity of somatosensory circuitry.
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Affiliation(s)
- Samantha E. Galindo
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Abby J. Wood
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA
| | - Patricia C. Cooney
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA
| | - Luke A. Hammond
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Wesley B. Grueber
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA
- Department of Physiology and Cellular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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20
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The times they are a-changin': a proposal on how brain flexibility goes beyond the obvious to include the concepts of "upward" and "downward" to neuroplasticity. Mol Psychiatry 2023; 28:977-992. [PMID: 36575306 PMCID: PMC10005965 DOI: 10.1038/s41380-022-01931-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022]
Abstract
Since the brain was found to be somehow flexible, plastic, researchers worldwide have been trying to comprehend its fundamentals to better understand the brain itself, make predictions, disentangle the neurobiology of brain diseases, and finally propose up-to-date treatments. Neuroplasticity is simple as a concept, but extremely complex when it comes to its mechanisms. This review aims to bring to light an aspect about neuroplasticity that is often not given enough attention as it should, the fact that the brain's ability to change would include its ability to disconnect synapses. So, neuronal shrinkage, decrease in spine density or dendritic complexity should be included within the concept of neuroplasticity as part of its mechanisms, not as an impairment of it. To that end, we extensively describe a variety of studies involving topics such as neurodevelopment, aging, stress, memory and homeostatic plasticity to highlight how the weakening and disconnection of synapses organically permeate the brain in so many ways as a good practice of its intrinsic physiology. Therefore, we propose to break down neuroplasticity into two sub-concepts, "upward neuroplasticity" for changes related to synaptic construction and "downward neuroplasticity" for changes related to synaptic deconstruction. With these sub-concepts, neuroplasticity could be better understood from a bigger landscape as a vector in which both directions could be taken for the brain to flexibly adapt to certain demands. Such a paradigm shift would allow a better understanding of the concept of neuroplasticity to avoid any data interpretation bias, once it makes clear that there is no morality with regard to the organic and physiological changes that involve dynamic biological systems as seen in the brain.
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21
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Ruach R, Ratner N, Emmons SW, Zaslaver A. The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations. Proc Natl Acad Sci U S A 2023; 120:e2201699120. [PMID: 36630454 PMCID: PMC9934027 DOI: 10.1073/pnas.2201699120] [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] [Indexed: 01/12/2023] Open
Abstract
Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.
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Affiliation(s)
- Rotem Ruach
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
| | - Nir Ratner
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
| | - Scott W. Emmons
- bDominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York10461, NY
- cDepartment of Genetics, Albert Einstein College of Medicine, New York10461, NY
| | - Alon Zaslaver
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
- 1To whom correspondence may be addressed.
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22
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Pagkalos M, Chavlis S, Poirazi P. Introducing the Dendrify framework for incorporating dendrites to spiking neural networks. Nat Commun 2023; 14:131. [PMID: 36627284 PMCID: PMC9832130 DOI: 10.1038/s41467-022-35747-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 12/22/2022] [Indexed: 01/11/2023] Open
Abstract
Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.
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Affiliation(s)
- Michalis Pagkalos
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece
- Department of Biology, University of Crete, Heraklion, 70013, Greece
| | - Spyridon Chavlis
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece.
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23
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Angular gyrus: an anatomical case study for association cortex. Brain Struct Funct 2023; 228:131-143. [PMID: 35906433 DOI: 10.1007/s00429-022-02537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/05/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.
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24
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Hopkins M, Fil J, Jones EG, Furber S. BitBrain and Sparse Binary Coincidence (SBC) memories: Fast, robust learning and inference for neuromorphic architectures. Front Neuroinform 2023; 17:1125844. [PMID: 37025552 PMCID: PMC10071999 DOI: 10.3389/fninf.2023.1125844] [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: 12/16/2022] [Accepted: 03/03/2023] [Indexed: 04/08/2023] Open
Abstract
We present an innovative working mechanism (the SBC memory) and surrounding infrastructure (BitBrain) based upon a novel synthesis of ideas from sparse coding, computational neuroscience and information theory that enables fast and adaptive learning and accurate, robust inference. The mechanism is designed to be implemented efficiently on current and future neuromorphic devices as well as on more conventional CPU and memory architectures. An example implementation on the SpiNNaker neuromorphic platform has been developed and initial results are presented. The SBC memory stores coincidences between features detected in class examples in a training set, and infers the class of a previously unseen test example by identifying the class with which it shares the highest number of feature coincidences. A number of SBC memories may be combined in a BitBrain to increase the diversity of the contributing feature coincidences. The resulting inference mechanism is shown to have excellent classification performance on benchmarks such as MNIST and EMNIST, achieving classification accuracy with single-pass learning approaching that of state-of-the-art deep networks with much larger tuneable parameter spaces and much higher training costs. It can also be made very robust to noise. BitBrain is designed to be very efficient in training and inference on both conventional and neuromorphic architectures. It provides a unique combination of single-pass, single-shot and continuous supervised learning; following a very simple unsupervised phase. Accurate classification inference that is very robust against imperfect inputs has been demonstrated. These contributions make it uniquely well-suited for edge and IoT applications.
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25
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Nebeling FC, Poll S, Justus LC, Steffen J, Keppler K, Mittag M, Fuhrmann M. Microglial motility is modulated by neuronal activity and correlates with dendritic spine plasticity in the hippocampus of awake mice. eLife 2023; 12:83176. [PMID: 36749020 PMCID: PMC9946443 DOI: 10.7554/elife.83176] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 02/03/2023] [Indexed: 02/08/2023] Open
Abstract
Microglia, the resident immune cells of the brain, play a complex role in health and disease. They actively survey the brain parenchyma by physically interacting with other cells and structurally shaping the brain. Yet, the mechanisms underlying microglial motility and significance for synapse stability, especially in the hippocampus during adulthood, remain widely unresolved. Here, we investigated the effect of neuronal activity on microglial motility and the implications for the formation and survival of dendritic spines on hippocampal CA1 neurons in vivo. We used repetitive two-photon in vivo imaging in the hippocampus of awake and anesthetized mice to simultaneously study the motility of microglia and their interaction with dendritic spines. We found that CA3 to CA1 input is sufficient to modulate microglial process motility. Simultaneously, more dendritic spines emerged in mice after awake compared to anesthetized imaging. Interestingly, the rate of microglial contacts with individual dendritic spines and dendrites was associated with the stability, removal, and emergence of dendritic spines. These results suggest that microglia might sense neuronal activity via neurotransmitter release and actively participate in synaptic rewiring of the hippocampal neural network during adulthood. Further, this study has profound relevance for hippocampal learning and memory processes.
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Affiliation(s)
| | - Stefanie Poll
- Neuroimmunology and Imaging Group, German Center for Neurodegenerative DiseasesBonnGermany
| | - Lena Christine Justus
- Neuroimmunology and Imaging Group, German Center for Neurodegenerative DiseasesBonnGermany
| | - Julia Steffen
- Neuroimmunology and Imaging Group, German Center for Neurodegenerative DiseasesBonnGermany
| | - Kevin Keppler
- Light Microscopy Facility, German Center for Neurodegenerative DiseasesBonnGermany
| | - Manuel Mittag
- Neuroimmunology and Imaging Group, German Center for Neurodegenerative DiseasesBonnGermany
| | - Martin Fuhrmann
- Neuroimmunology and Imaging Group, German Center for Neurodegenerative DiseasesBonnGermany
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26
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Gu Z, Chen H, Zhao H, Yang W, Song Y, Li X, Wang Y, Du D, Liao H, Pan W, Li X, Gao Y, Han H, Tong Z. New insight into brain disease therapy: nanomedicines-crossing blood-brain barrier and extracellular space for drug delivery. Expert Opin Drug Deliv 2022; 19:1618-1635. [PMID: 36285632 DOI: 10.1080/17425247.2022.2139369] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
INTRODUCTION Brain diseases including brain tumor, Alzheimer's disease, Parkinson's disease, etc. are difficult to treat. The blood-brain barrier (BBB) is a major obstacle for drug delivery into the brain. Although nano-package and receptor-mediated delivery of nanomedicine markedly increases BBB penetration, it yet did not extensively improve clinical cure rate. Recently, brain extracellular space (ECS) and interstitial fluid (ISF) drainage in ECS have been found to determine whether a drug dissolved in ISF can reach its target cells. Notably, an increase in tortuosity of ECS associated with slower ISF drainage induced by the accumulated harmful substances, such as: amyloid-beta (Aβ), α-synuclein, and metabolic wastes, causes drug delivery failure. AREAS COVERED The methods of nano-package and receptor-mediated drug delivery and the penetration efficacy of nanomedicines across BBB and ECS are assessed. EXPERT OPINION Invasive delivering drug via ECS and noninvasive near-infrared photo-sensitive nanomedicines may provide a promising benefit to patients with brain disease.
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Affiliation(s)
- Ziqi Gu
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Haishu Chen
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Han Zhao
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Wanting Yang
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Yilan Song
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Xiang Li
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Yang Wang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Dan Du
- Department of Radiology, Peking University Third Hospital, Beijing, China.,Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China.,Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China
| | - Haikang Liao
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Wenhao Pan
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Xi Li
- The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yajuan Gao
- Department of Radiology, Peking University Third Hospital, Beijing, China.,NMPA key Laboratory for Evaluation of Medical Imaging Equipment and Technique, Beijing, China
| | - Hongbin Han
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,Department of Radiology, Peking University Third Hospital, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China.,Peking University Shenzhen Graduate School, Shenzhen, China
| | - Zhiqian Tong
- Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Oujiang Laboratory, School of Mental Health, Wenzhou Medical University, Wenzhou, China.,The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, China
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27
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Dorman DB, Blackwell KT. Synaptic Plasticity Is Predicted by Spatiotemporal Firing Rate Patterns and Robust to In Vivo-like Variability. Biomolecules 2022; 12:1402. [PMID: 36291612 PMCID: PMC9599115 DOI: 10.3390/biom12101402] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/13/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Abstract
Synaptic plasticity, the experience-induced change in connections between neurons, underlies learning and memory in the brain. Most of our understanding of synaptic plasticity derives from in vitro experiments with precisely repeated stimulus patterns; however, neurons exhibit significant variability in vivo during repeated experiences. Further, the spatial pattern of synaptic inputs to the dendritic tree influences synaptic plasticity, yet is not considered in most synaptic plasticity rules. Here, we investigate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a plasticity rule based on calcium dynamics. Using in vivo spike train recordings as inputs to different size clusters of spines, we show that plasticity is strongly robust to trial-to-trial variability of spike timing. In addition, we derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity. Synapses that strongly potentiated have greater firing rates and calcium concentration later in the trial, whereas strongly depressing synapses have hiring firing rates early in the trial. The neighboring synaptic activity influences the direction and magnitude of synaptic plasticity, with small clusters of spines producing the greatest increase in synaptic strength. Together, our results reveal that calcium dynamics can unify diverse plasticity rules and reveal how spatiotemporal firing rate patterns control synaptic plasticity.
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Affiliation(s)
- Daniel B. Dorman
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA 22030, USA
| | - Kim T. Blackwell
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA 22030, USA
- Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
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28
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Amano R, Nakao M, Matsumiya K, Miwakeichi F. A computational model to explore how temporal stimulation patterns affect synapse plasticity. PLoS One 2022; 17:e0275059. [PMID: 36149886 PMCID: PMC9506666 DOI: 10.1371/journal.pone.0275059] [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: 02/11/2022] [Accepted: 09/09/2022] [Indexed: 11/18/2022] Open
Abstract
Plasticity-related proteins (PRPs), which are synthesized in a synapse activation-dependent manner, are shared by multiple synapses to a limited spatial extent for a specific period. In addition, stimulated synapses can utilize shared PRPs through synaptic tagging and capture (STC). In particular, the phenomenon by which short-lived early long-term potentiation is transformed into long-lived late long-term potentiation using shared PRPs is called “late-associativity,” which is the underlying principle of “cluster plasticity.” We hypothesized that the competitive capture of PRPs by multiple synapses modulates late-associativity and affects the fate of each synapse in terms of whether it is integrated into a synapse cluster. We tested our hypothesis by developing a computational model to simulate STC, late-associativity, and the competitive capture of PRPs. The experimental results obtained using the model revealed that the number of competing synapses, timing of stimulation to each synapse, and basal PRP level in the dendritic compartment altered the effective temporal window of STC and influenced the conditions under which late-associativity occurs. Furthermore, it is suggested that the competitive capture of PRPs results in the selection of synapses to be integrated into a synapse cluster via late-associativity.
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Affiliation(s)
- Ryota Amano
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
- * E-mail:
| | - Mitsuyuki Nakao
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | | | - Fumikazu Miwakeichi
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
- Department of Statistical Modeling, The Institute of Statistical Mathematics, Tachikawa-Shi, Japan
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29
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Chen M, Qi J, Poo M, Yang Y. Stability and dynamics of dendritic spines in macaque prefrontal cortex. Natl Sci Rev 2022; 9:nwac125. [PMID: 36196248 PMCID: PMC9521340 DOI: 10.1093/nsr/nwac125] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/08/2022] [Accepted: 06/15/2022] [Indexed: 11/16/2022] Open
Abstract
Formation and elimination of synapses reflect structural plasticity of neuronal connectivity. Here we performed high-resolution two-photon imaging of dendritic spines in the prefrontal cortex of four macaque monkeys and found that spines were in general highly stable, with low percentages undergoing synaptic turnover. By observing the same spines at weekly intervals, we found that newly formed spines were more susceptible to elimination, with only 40% persisting over a period of months. Analyses of spatial distribution of large numbers of spines revealed that spine distribution was neither uniform nor random, favoring inter-spine distances of 2–4 μm. Furthermore, spine formation and elimination occurred more often in low- and high-density dendritic segments, respectively, and preferentially within a hot zone of ∼4 μm from existing spines. Our results demonstrate long-term stability and spatially regulated spine dynamics in the macaque cortex and provide a structural basis for understanding neural circuit plasticity in the primate brain.
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30
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Hedrick NG, Lu Z, Bushong E, Singhi S, Nguyen P, Magaña Y, Jilani S, Lim BK, Ellisman M, Komiyama T. Learning binds new inputs into functional synaptic clusters via spinogenesis. Nat Neurosci 2022; 25:726-737. [PMID: 35654957 DOI: 10.1038/s41593-022-01086-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/26/2022] [Indexed: 11/08/2022]
Abstract
Learning induces the formation of new excitatory synapses in the form of dendritic spines, but their functional properties remain unknown. Here, using longitudinal in vivo two-photon imaging and correlated electron microscopy of dendritic spines in the motor cortex of mice during motor learning, we describe a framework for the formation, survival and resulting function of new, learning-related spines. Specifically, our data indicate that the formation of new spines during learning is guided by the potentiation of functionally clustered preexisting spines exhibiting task-related activity during earlier sessions of learning. We present evidence that this clustered potentiation induces the local outgrowth of multiple filopodia from the nearby dendrite, locally sampling the adjacent neuropil for potential axonal partners, likely via targeting preexisting presynaptic boutons. Successful connections are then selected for survival based on co-activity with nearby task-related spines, ensuring that the new spine preserves functional clustering. The resulting locally coherent activity of new spines signals the learned movement. Furthermore, we found that a majority of new spines synapse with axons previously unrepresented in these dendritic domains. Thus, learning involves the binding of new information streams into functional synaptic clusters to subserve learned behaviors.
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Affiliation(s)
- Nathan G Hedrick
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA.
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA.
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA.
| | - Zhongmin Lu
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Eric Bushong
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Center for Research in Biological Systems, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- National Center for Microscopy and Imaging Research, University of California, San Diego, La Jolla, CA, USA
| | - Surbhi Singhi
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Peter Nguyen
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Yessenia Magaña
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Sayyed Jilani
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Byung Kook Lim
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA
| | - Mark Ellisman
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Center for Research in Biological Systems, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- National Center for Microscopy and Imaging Research, University of California, San Diego, La Jolla, CA, USA
| | - Takaki Komiyama
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.
- Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, USA.
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA.
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA.
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31
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Introduction. Neuroscience 2022; 489:1-3. [DOI: 10.1016/j.neuroscience.2022.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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32
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Pham VM, Thakor N. Insulin enhances neurite extension and myelination of diabetic neuropathy neurons. Korean J Pain 2022; 35:160-172. [PMID: 35354679 PMCID: PMC8977202 DOI: 10.3344/kjp.2022.35.2.160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 11/24/2022] Open
Abstract
Background The authors established an in vitro model of diabetic neuropathy based on the culture system of primary neurons and Schwann cells (SCs) to mimic similar symptoms observed in in vivo models of this complication, such as impaired neurite extension and impaired myelination. The model was then utilized to investigate the effects of insulin on enhancing neurite extension and myelination of diabetic neurons. Methods SCs and primary neurons were cultured under conditions mimicking hyperglycemia prepared by adding glucose to the basal culture medium. In a single culture, the proliferation and maturation of SCs and the neurite extension of neurons were evaluated. In a co-culture, the percentage of myelination of diabetic neurons was investigated. Insulin at different concentrations was supplemented to culture media to examine its effects on neurite extension and myelination. Results The cells showed similar symptoms observed in in vivo models of this complication. In a single culture, hyperglycemia attenuated the proliferation and maturation of SCs, induced apoptosis, and impaired neurite extension of both sensory and motor neurons. In a co-culture of SCs and neurons, the percentage of myelinated neurites in the hyperglycemia-treated group was significantly lower than that in the control group. This impaired neurite extension and myelination was reversed by the introduction of insulin to the hyperglycemic culture media. Conclusions Insulin may be a potential candidate for improving diabetic neuropathy. Insulin can function as a neurotrophic factor to support both neurons and SCs. Further research is needed to discover the potential of insulin in improving diabetic neuropathy.
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Affiliation(s)
- Vuong M Pham
- Singapore Institute for Neurotechnology, National University of Singapore, Singapore.,Department of Biotechnology, Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam
| | - Nitish Thakor
- Singapore Institute for Neurotechnology, National University of Singapore, Singapore.,Department of Biomedical Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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33
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Larkum M. Are dendrites conceptually useful? Neuroscience 2022; 489:4-14. [DOI: 10.1016/j.neuroscience.2022.03.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 02/10/2022] [Accepted: 03/05/2022] [Indexed: 12/13/2022]
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34
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Kirchner JH, Gjorgjieva J. Emergence of synaptic organization and computation in dendrites. NEUROFORUM 2022; 28:21-30. [PMID: 35881644 PMCID: PMC8887907 DOI: 10.1515/nf-2021-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.
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Affiliation(s)
- Jan H. Kirchner
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Max-von-Laue-Str. 4, 60438Frankfurt, Germany
- Technical University of Munich, School of Life Sciences, 85354Freising, Germany
| | - Julijana Gjorgjieva
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Max-von-Laue-Str. 4, 60438Frankfurt, Germany
- Technical University of Munich, School of Life Sciences, 85354Freising, Germany
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35
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Braun W, Memmesheimer RM. High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1. PLoS Comput Biol 2022; 18:e1009891. [PMID: 35176028 PMCID: PMC8890743 DOI: 10.1371/journal.pcbi.1009891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 03/02/2022] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.
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Affiliation(s)
- Wilhelm Braun
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail: (WB); (R-MM)
| | - Raoul-Martin Memmesheimer
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- * E-mail: (WB); (R-MM)
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36
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Local dendritic balance enables learning of efficient representations in networks of spiking neurons. Proc Natl Acad Sci U S A 2021; 118:2021925118. [PMID: 34876505 PMCID: PMC8685685 DOI: 10.1073/pnas.2021925118] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2021] [Indexed: 11/18/2022] Open
Abstract
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.
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37
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Reyes-Resina I, Samer S, Kreutz MR, Oelschlegel AM. Molecular Mechanisms of Memory Consolidation That Operate During Sleep. Front Mol Neurosci 2021; 14:767384. [PMID: 34867190 PMCID: PMC8636908 DOI: 10.3389/fnmol.2021.767384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 10/27/2021] [Indexed: 11/17/2022] Open
Abstract
The role of sleep for brain function has been in the focus of interest for many years. It is now firmly established that sleep and the corresponding brain activity is of central importance for memory consolidation. Less clear are the underlying molecular mechanisms and their specific contribution to the formation of long-term memory. In this review, we summarize the current knowledge of such mechanisms and we discuss the several unknowns that hinder a deeper appreciation of how molecular mechanisms of memory consolidation during sleep impact synaptic function and engram formation.
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Affiliation(s)
- Irene Reyes-Resina
- Research Group Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Sebastian Samer
- Research Group Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Michael R Kreutz
- Research Group Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Leibniz Group 'Dendritic Organelles and Synaptic Function', Center for Molecular Neurobiology, ZMNH, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Anja M Oelschlegel
- Research Group Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany
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Tzilivaki A, Kastellakis G, Schmitz D, Poirazi P. GABAergic Interneurons with Nonlinear Dendrites: From Neuronal Computations to Memory Engrams. Neuroscience 2021; 489:34-43. [PMID: 34843894 DOI: 10.1016/j.neuroscience.2021.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 11/08/2021] [Accepted: 11/22/2021] [Indexed: 10/19/2022]
Abstract
GABAergic interneurons (INs) are a highly diverse class of neurons in the mammalian brain with a critical role in orchestrating multiple cognitive functions and maintaining the balance of excitation/inhibition across neuronal circuitries. In this perspective, we discuss recent findings regarding the ability of some IN subtypes to integrate incoming inputs in nonlinear ways within their dendritic branches. These recently discovered features may endow the specific INs with advanced computing capabilities, whose breadth and functional contributions remain an open question. Along these lines, we discuss theoretical and experimental evidence regarding the potential role of nonlinear IN dendrites in advancing single neuron computations and contributing to memory formation.
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Affiliation(s)
- Alexandra Tzilivaki
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany; Neurocure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany; Foundation for Research and Technology Hellas, Institute of Molecular Biology and Biotechnology, Greece
| | - George Kastellakis
- Foundation for Research and Technology Hellas, Institute of Molecular Biology and Biotechnology, Greece
| | - Dietmar Schmitz
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany; Neurocure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany
| | - Panayiota Poirazi
- Foundation for Research and Technology Hellas, Institute of Molecular Biology and Biotechnology, Greece.
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A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron 2021; 109:4001-4017.e10. [PMID: 34715026 PMCID: PMC8691952 DOI: 10.1016/j.neuron.2021.09.044] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/10/2021] [Accepted: 09/23/2021] [Indexed: 11/23/2022]
Abstract
Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.
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Acharya J, Basu A, Legenstein R, Limbacher T, Poirazi P, Wu X. Dendritic Computing: Branching Deeper into Machine Learning. Neuroscience 2021; 489:275-289. [PMID: 34656706 DOI: 10.1016/j.neuroscience.2021.10.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 09/07/2021] [Accepted: 10/03/2021] [Indexed: 12/31/2022]
Abstract
In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.
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Affiliation(s)
| | - Arindam Basu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong
| | - Robert Legenstein
- Institute of Theoretical Computer Science, Graz University of Technology, Austria
| | - Thomas Limbacher
- Institute of Theoretical Computer Science, Graz University of Technology, Austria
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Greece
| | - Xundong Wu
- School of Computer Science, Hangzhou Dianzi University, China
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41
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Brandalise F, Carta S, Leone R, Helmchen F, Holtmaat A, Gerber U. Dendritic Branch-constrained N-Methyl-d-Aspartate Receptor-mediated Spikes Drive Synaptic Plasticity in Hippocampal CA3 Pyramidal Cells. Neuroscience 2021; 489:57-68. [PMID: 34634424 DOI: 10.1016/j.neuroscience.2021.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/27/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
N-methyl-d-aspartate receptor-mediated ( spikes can be causally linked to the induction of synaptic long-term potentiation (LTP) in hippocampal and cortical pyramidal cells. However, it is unclear if they regulate plasticity at a local or global scale in the dendritic tree. Here, we used dendritic patch-clamp recordings and calcium imaging to investigate the integrative properties of single dendrites of hippocampal CA3 cells. We show that local hyperpolarization of a single dendritic segment prevents NMDA spikes, their associated calcium transients, as well as LTP in a branch-specific manner. This result provides direct, causal evidence that the single dendritic branch can operate as a functional unit in regulating CA3 pyramidal cell plasticity.
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Affiliation(s)
- Federico Brandalise
- Department of Basic Neurosciences and the Center for Neuroscience, Centre Médical Universitaire (CMU), University of Geneva, 1211 Geneva, Switzerland; Former affiliation(b).
| | - Stefano Carta
- Brain Research Institute and Neuroscience Center Zurich, University of Zurich, CH-8057 Zurich, Switzerland
| | - Roberta Leone
- Department of Basic Neurosciences and the Center for Neuroscience, Centre Médical Universitaire (CMU), University of Geneva, 1211 Geneva, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute and Neuroscience Center Zurich, University of Zurich, CH-8057 Zurich, Switzerland
| | - Anthony Holtmaat
- Department of Basic Neurosciences and the Center for Neuroscience, Centre Médical Universitaire (CMU), University of Geneva, 1211 Geneva, Switzerland
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Pulikkottil VV, Somashekar BP, Bhalla US. Computation, wiring, and plasticity in synaptic clusters. Curr Opin Neurobiol 2021; 70:101-112. [PMID: 34509808 DOI: 10.1016/j.conb.2021.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 07/28/2021] [Accepted: 08/09/2021] [Indexed: 01/19/2023]
Abstract
Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano-chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.
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Affiliation(s)
| | - Bhanu Priya Somashekar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Upinder S Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.
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43
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Beniaguev D, Segev I, London M. Single cortical neurons as deep artificial neural networks. Neuron 2021; 109:2727-2739.e3. [PMID: 34380016 DOI: 10.1016/j.neuron.2021.07.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/04/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022]
Abstract
Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical models of cortical neurons at millisecond (spiking) resolution. A temporally convolutional DNN with five to eight layers was required to capture the I/O mapping of a realistic model of a layer 5 cortical pyramidal cell (L5PC). This DNN generalized well when presented with inputs widely outside the training distribution. When NMDA receptors were removed, a much simpler network (fully connected neural network with one hidden layer) was sufficient to fit the model. Analysis of the DNNs' weight matrices revealed that synaptic integration in dendritic branches could be conceptualized as pattern matching from a set of spatiotemporal templates. This study provides a unified characterization of the computational complexity of single neurons and suggests that cortical networks therefore have a unique architecture, potentially supporting their computational power.
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Affiliation(s)
- David Beniaguev
- Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Michael London
- Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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44
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Harkin EF, Shen PR, Goel A, Richards BA, Naud R. Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Sub-cellular Computation. Neuroscience 2021; 489:200-215. [PMID: 34358629 DOI: 10.1016/j.neuroscience.2021.07.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/06/2021] [Accepted: 07/25/2021] [Indexed: 11/15/2022]
Abstract
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
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Affiliation(s)
- Emerson F Harkin
- uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Peter R Shen
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Anish Goel
- Lisgar Collegiate Institute, Ottawa, ON, Canada
| | - Blake A Richards
- Mila, Montréal, QC, Canada; Montreal Neurological Institute, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada; School of Computer Science, McGill University, Montréal, QC, Canada.
| | - Richard Naud
- uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada; Department of Physics, University of Ottawa, Ottawa, ON, Canada.
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45
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Güler M. Multibranch Formal Neuron: An Internally Nonlinear Learning Unit. Neural Comput 2021; 33:2736-2761. [PMID: 34280300 DOI: 10.1162/neco_a_01428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/18/2021] [Indexed: 11/04/2022]
Abstract
The transformation of synaptic input into action potential in nerve cells is strongly influenced by the morphology of the dendritic arbor as well as the synaptic efficacy map. The multiplicity of dendritic branches strikingly enables a single cell to act as a highly nonlinear processing element. Studies have also found functional synaptic clustering whereby synapses that encode a common sensory feature are spatially clustered together on the branches. Motivated by these findings, here we introduce a multibranch formal model of the neuron that can integrate synaptic inputs nonlinearly through collective action of its dendritic branches and yields synaptic clustering. An analysis in support of its use as a computational building block is offered. Also offered is an accompanying gradient descent-based learning algorithm. The model unit spans a wide spectrum of nonlinearities, including the parity problem, and can outperform the multilayer perceptron in generalizing to unseen data. The occurrence of synaptic clustering boosts the generalization efficiency of the unit, which may also be the answer for the puzzling ubiquity of synaptic clustering in the real neurons. Our theoretical analysis is backed up by simulations. The study could pave the way to new artificial neural networks.
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Affiliation(s)
- Marifi Güler
- Department of Computer Engineering, Eastern Mediterranean University, 99628 Famagusta North Cyprus, Turkey
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46
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Guo W, Tang ZY, Cai ZY, Zhao WE, Yang J, Wang XP, Ji J, Huang XX, Sun XL. Iptakalim alleviates synaptic damages via targeting mitochondrial ATP-sensitive potassium channel in depression. FASEB J 2021; 35:e21581. [PMID: 33871072 DOI: 10.1096/fj.202100124rr] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/18/2021] [Accepted: 03/25/2021] [Indexed: 12/28/2022]
Abstract
Synaptic plasticity damages play a crucial role in the onset and development of depression, especially in the hippocampus, which is more susceptible to stress and the most frequently studied brain region in depression. And, mitochondria have a major function in executing the complex processes of neurotransmission and plasticity. We have previously demonstrated that Iptakalim (Ipt), a new ATP-sensitive potassium (K-ATP) channel opener, could improve the depressive-like behavior in mice. But the underlying mechanisms are not well understood. The present study demonstrated that Ipt reversed depressive-like phenotype in vivo (chronic mild stress-induced mice model of depression) and in vitro (corticosterone-induced cellular model). Further study showed that Ipt could upregulate the synaptic-related proteins postsynaptic density 95 (PSD 95) and synaptophysin (SYN), and alleviated the synaptic structure damage. Moreover, Ipt could reverse the abnormal mitochondrial fission and fusion, as well as the reduced mitochondrial ATP production and collapse of mitochondrial membrane potential in depressive models. Knocking down the mitochondrial ATP-sensitive potassium (Mito-KATP) channel subunit MitoK partly blocked the above effects of Ipt. Therefore, our results reveal that Ipt can alleviate the abnormal mitochondrial dynamics and function depending on MitoK, contributing to improve synaptic plasticity and exert antidepressive effects. These findings provide a candidate compound and a novel target for antidepressive therapy.
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Affiliation(s)
- Wei Guo
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zi-Yang Tang
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhen-Yu Cai
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Wen-E Zhao
- Analysis Center, Nanjing Medical University, Nanjing, China
| | - Jin Yang
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Xi-Peng Wang
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Juan Ji
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Xin-Xin Huang
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Xiu-Lan Sun
- Neuroprotective Drug Discovery Key Laboratory, Jiangsu Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, China.,The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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47
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Kirchner JH, Gjorgjieva J. Emergence of local and global synaptic organization on cortical dendrites. Nat Commun 2021; 12:4005. [PMID: 34183661 PMCID: PMC8239006 DOI: 10.1038/s41467-021-23557-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/03/2021] [Indexed: 02/06/2023] Open
Abstract
Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales.
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Affiliation(s)
- Jan H. Kirchner
- grid.419505.c0000 0004 0491 3878Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany ,grid.6936.a0000000123222966School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Julijana Gjorgjieva
- grid.419505.c0000 0004 0491 3878Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany ,grid.6936.a0000000123222966School of Life Sciences, Technical University of Munich, Freising, Germany
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48
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Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a deterioration of neuronal connectivity. The pathological accumulation of tau in neurons is one of the hallmarks of AD and has been connected to the loss of dendritic spines of pyramidal cells, which are the major targets of cortical excitatory synapses and key elements in memory storage. However, the detailed mechanisms underlying the loss of dendritic spines in individuals with AD are still unclear. Here, we used graph-theory approaches to compare the distribution of dendritic spines from neurons with and without tau pathology of AD individuals. We found that the presence of tau pathology determines the loss of dendritic spines in clusters, ruling out alternative models where spine loss occurs at random locations. Since memory storage has been associated with synaptic clusters, the present results provide a new insight into the mechanisms by which tau drives synaptic damage in AD, paving the way to memory deficits through alterations of spine organization.
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49
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Moldwin T, Kalmenson M, Segev I. The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent. PLoS Comput Biol 2021; 17:e1009015. [PMID: 34029309 PMCID: PMC8177649 DOI: 10.1371/journal.pcbi.1009015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 06/04/2021] [Accepted: 04/28/2021] [Indexed: 02/01/2023] Open
Abstract
Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via nonlinear voltage-dependent mechanisms, such as NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of "under-performing" synapses on a model dendrite during learning ("structural plasticity"), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location-dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the all-versus-all MNIST task (~85%) approaches that of logistic regression (~93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron ("functional plasticity") and show that a G-clusteron that utilizes the weight update rule can achieve ~89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions.
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Affiliation(s)
- Toviah Moldwin
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
- * E-mail:
| | - Menachem Kalmenson
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel
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50
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Ma S, Zuo Y. Synaptic modifications in learning and memory - A dendritic spine story. Semin Cell Dev Biol 2021; 125:84-90. [PMID: 34020876 DOI: 10.1016/j.semcdb.2021.05.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/06/2021] [Accepted: 05/12/2021] [Indexed: 11/15/2022]
Abstract
Synapses are specialized sites where neurons connect and communicate with each other. Activity-dependent modification of synaptic structure and function provides a mechanism for learning and memory. The advent of high-resolution time-lapse imaging in conjunction with fluorescent biosensors and actuators enables researchers to monitor and manipulate the structure and function of synapses both in vitro and in vivo. This review focuses on recent imaging studies on the synaptic modification underlying learning and memory.
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Affiliation(s)
- Shaorong Ma
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
| | - Yi Zuo
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.
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