1
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Higashi R, Morita M, Kawaguchi SY. Cl --dependent amplification of excitatory synaptic potentials at distal dendrites revealed by voltage imaging. SCIENCE ADVANCES 2024; 10:eadj2547. [PMID: 39196927 PMCID: PMC11352850 DOI: 10.1126/sciadv.adj2547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/25/2024] [Indexed: 08/30/2024]
Abstract
The processing of synaptic signals in somatodendritic compartments determines neuronal computation. Although the amplification of excitatory signals by local voltage-dependent cation channels has been extensively studied, their spatiotemporal dynamics in elaborate dendritic branches remain obscure owing to technical limitations. Using fluorescent voltage imaging throughout dendritic arborizations in hippocampal pyramidal neurons, we demonstrate a unique chloride ion (Cl-)-dependent remote computation mechanism in the distal branches. Excitatory postsynaptic potentials triggered by local laser photolysis of caged glutamate spread along dendrites, with gradual amplification toward the distal end while attenuation toward the soma. Tour de force subcellular patch-clamp recordings from thin branches complemented by biophysical model simulations revealed that the asymmetric augmentation of excitation relies on tetrodotoxin-resistant sodium ion (Na+) channels and Cl- conductance accompanied by a more hyperpolarized dendritic resting potential. Together, this study reveals the cooperative voltage-dependent actions of cation and anion conductance for dendritic supralinear computation, which can locally decode the spatiotemporal context of synaptic inputs.
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2
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Xiao S, Yadav S, Jayant K. Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging. Cell Rep 2024; 43:114413. [PMID: 38943640 DOI: 10.1016/j.celrep.2024.114413] [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: 09/29/2022] [Revised: 05/06/2024] [Accepted: 06/12/2024] [Indexed: 07/01/2024] Open
Abstract
Basal dendrites of layer 5 cortical pyramidal neurons exhibit Na+ and N-methyl-D-aspartate receptor (NMDAR) regenerative spikes and are uniquely poised to influence somatic output. Nevertheless, due to technical limitations, how multibranch basal dendritic integration shapes and enables multiplexed barcoding of synaptic streams remains poorly mapped. Here, we combine 3D two-photon holographic transmitter uncaging, whole-cell dynamic clamp, and biophysical modeling to reveal how synchronously activated synapses (distributed and clustered) across multiple basal dendritic branches are multiplexed under quiescent and in vivo-like conditions. While dendritic regenerative Na+ spikes promote millisecond somatic spike precision, distributed synaptic inputs and NMDAR spikes regulate gain. These concomitantly occurring dendritic nonlinearities enable multiplexed information transfer amid an ongoing noisy background, including under back-propagating voltage resets, by barcoding the axo-somatic spike structure. Our results unveil a multibranch dendritic integration framework in which dendritic nonlinearities are critical for multiplexing different spatial-temporal synaptic input patterns, enabling optimal feature binding.
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Affiliation(s)
- Shulan Xiao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Saumitra Yadav
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Krishna Jayant
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA.
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3
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Horton S, Mastrolia V, Jackson RE, Kemlo S, Pereira Machado PM, Carbajal MA, Hindges R, Fleck RA, Aguiar P, Neves G, Burrone J. Excitatory and inhibitory synapses show a tight subcellular correlation that weakens over development. Cell Rep 2024; 43:114361. [PMID: 38900634 DOI: 10.1016/j.celrep.2024.114361] [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: 02/09/2024] [Revised: 04/24/2024] [Accepted: 05/30/2024] [Indexed: 06/22/2024] Open
Abstract
Neurons receive correlated levels of excitation and inhibition, a feature that is important for proper brain function. However, how this relationship between excitatory and inhibitory inputs is established during the dynamic period of circuit wiring remains unexplored. Using multiple techniques, including in utero electroporation, electron microscopy, and electrophysiology, we reveal a tight correlation in the distribution of excitatory and inhibitory synapses along the dendrites of developing CA1 hippocampal neurons. This correlation was present within short dendritic stretches (<20 μm) and, surprisingly, was most pronounced during early development, sharply declining with maturity. The tight matching between excitation and inhibition was unexpected, as inhibitory synapses lacked an active zone when formed and exhibited compromised evoked release. We propose that inhibitory synapses form as a stabilizing scaffold to counterbalance growing excitation levels. This relationship diminishes over time, suggesting a critical role for a subcellular balance in early neuronal function and circuit formation.
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Affiliation(s)
- Sally Horton
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK
| | - Vincenzo Mastrolia
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK
| | - Rachel E Jackson
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK
| | - Sarah Kemlo
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK
| | - Pedro M Pereira Machado
- Centre for Ultrastructural Imaging (CUI), Kings College London, New Hunts House, Guys Hospital Campus, London SE1 1UL, UK
| | - Maria Alejandra Carbajal
- Centre for Ultrastructural Imaging (CUI), Kings College London, New Hunts House, Guys Hospital Campus, London SE1 1UL, UK
| | - Robert Hindges
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK
| | - Roland A Fleck
- Centre for Ultrastructural Imaging (CUI), Kings College London, New Hunts House, Guys Hospital Campus, London SE1 1UL, UK
| | - Paulo Aguiar
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Guilherme Neves
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK.
| | - Juan Burrone
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK.
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4
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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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5
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Jauch J, Becker M, Tetzlaff C, Fauth MJ. Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites. PLoS Comput Biol 2024; 20:e1012218. [PMID: 38917228 PMCID: PMC11230591 DOI: 10.1371/journal.pcbi.1012218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 07/08/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.
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Affiliation(s)
- Jannik Jauch
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
| | - Moritz Becker
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Tetzlaff
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | - Michael Jan Fauth
- Third Institute for Physics, Georg-August-University, Göttingen, Germany
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6
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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: 2] [Impact Index Per Article: 2.0] [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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7
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Levy WB, Baxter RA. Growing dendrites enhance a neuron's computational power and memory capacity. Neural Netw 2023; 164:275-309. [PMID: 37163846 DOI: 10.1016/j.neunet.2023.04.033] [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: 07/20/2022] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/12/2023]
Abstract
Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, homeostatic, Hebbian developmental theory. Here we investigate the neurocomputational advantages and limits on this novel algorithm that combines dendritogenesis with supervised adaptive synaptogenesis. Neurons created with this algorithm have enhanced memory capacity, can avoid catastrophic interference (forgetting), and have the ability to unmix mixture distributions. In particular, individual dendrites develop within each class, in an unsupervised manner, to become feature-clusters that correspond to the mixing elements of class-conditional mixture distribution. Error-free classification is demonstrated with input perturbations up to 40%. Although discriminative problems are used to understand the capabilities of the stochastic algorithm and the neuronal connectivity it produces, the algorithm is in the generative class, it thus seems ideal for decisions that require generalization, i.e., extrapolation beyond previous learning.
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Affiliation(s)
- William B Levy
- Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA 22908, United States of America; Informed Simplifications, Earlysville, VA 22936, United States of America.
| | - Robert A Baxter
- Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA 22908, United States of America; Baxter Adaptive Systems, Bedford, MA 01730, United States of America
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8
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Kim YJ, Ujfalussy BB, Lengyel M. Parallel functional architectures within a single dendritic tree. Cell Rep 2023; 42:112386. [PMID: 37060564 PMCID: PMC7614531 DOI: 10.1016/j.celrep.2023.112386] [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: 02/24/2022] [Revised: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/16/2023] Open
Abstract
The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, such that each synaptic input makes a single contribution to the neuronal response. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable, models of the transformation of input spikes into the somatic "output" voltage and to automatically select among alternative functional architectures. With dendritic Na+ channels blocked, responses are accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+-dependent integration requires a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporate distinct morphological and biophysical properties of the neuron and its synaptic organization.
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Affiliation(s)
- Young Joon Kim
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Harvard Medical School, Boston, MA, USA.
| | - Balázs B Ujfalussy
- Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
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9
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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: 8] [Impact Index Per Article: 8.0] [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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10
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Rasia-Filho AA, Calcagnotto ME, von Bohlen Und Halbach O. Introduction: What Are Dendritic Spines? ADVANCES IN NEUROBIOLOGY 2023; 34:1-68. [PMID: 37962793 DOI: 10.1007/978-3-031-36159-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dendritic spines are cellular specializations that greatly increase the connectivity of neurons and modulate the "weight" of most postsynaptic excitatory potentials. Spines are found in very diverse animal species providing neural networks with a high integrative and computational possibility and plasticity, enabling the perception of sensorial stimuli and the elaboration of a myriad of behavioral displays, including emotional processing, memory, and learning. Humans have trillions of spines in the cerebral cortex, and these spines in a continuum of shapes and sizes can integrate the features that differ our brain from other species. In this chapter, we describe (1) the discovery of these small neuronal protrusions and the search for the biological meaning of dendritic spines; (2) the heterogeneity of shapes and sizes of spines, whose structure and composition are associated with the fine-tuning of synaptic processing in each nervous area, as well as the findings that support the role of dendritic spines in increasing the wiring of neural circuits and their functions; and (3) within the intraspine microenvironment, the integration and activation of signaling biochemical pathways, the compartmentalization of molecules or their spreading outside the spine, and the biophysical properties that can affect parent dendrites. We also provide (4) examples of plasticity involving dendritic spines and neural circuits relevant to species survival and comment on (5) current research advancements and challenges in this exciting research field.
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Affiliation(s)
- Alberto A Rasia-Filho
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brazil
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Elisa Calcagnotto
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Psychiatry and Behavioral Science, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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11
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An astrocytic signaling loop for frequency-dependent control of dendritic integration and spatial learning. Nat Commun 2022; 13:7932. [PMID: 36566254 PMCID: PMC9789958 DOI: 10.1038/s41467-022-35620-8] [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: 01/04/2022] [Accepted: 12/13/2022] [Indexed: 12/25/2022] Open
Abstract
Dendrites of hippocampal CA1 pyramidal cells amplify clustered glutamatergic input by activation of voltage-gated sodium channels and N-methyl-D-aspartate receptors (NMDARs). NMDAR activity depends on the presence of NMDAR co-agonists such as D-serine, but how co-agonists influence dendritic integration is not well understood. Using combinations of whole-cell patch clamp, iontophoretic glutamate application, two-photon excitation fluorescence microscopy and glutamate uncaging in acute rat and mouse brain slices we found that exogenous D-serine reduced the threshold of dendritic spikes and increased their amplitude. Triggering an astrocytic mechanism controlling endogenous D-serine supply via endocannabinoid receptors (CBRs) also increased dendritic spiking. Unexpectedly, this pathway was activated by pyramidal cell activity primarily in the theta range, which required HCN channels and astrocytic CB1Rs. Therefore, astrocytes close a positive and frequency-dependent feedback loop between pyramidal cell activity and their integration of dendritic input. Its disruption in mice led to an impairment of spatial memory, which demonstrated its behavioral relevance.
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12
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Rindner DJ, Proddutur A, Lur G. Cell-type-specific integration of feedforward and feedback synaptic inputs in the posterior parietal cortex. Neuron 2022; 110:3760-3773.e5. [PMID: 36087582 PMCID: PMC9671855 DOI: 10.1016/j.neuron.2022.08.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 07/19/2022] [Accepted: 08/16/2022] [Indexed: 12/15/2022]
Abstract
The integration of feedforward (sensory) and feedback (top-down) neuronal signals is a principal function of the neocortex. Yet, we have limited insight into how these information streams are combined by individual neurons. Using a two-color optogenetic strategy, we found that layer 5 pyramidal neurons in the posterior parietal cortex receive monosynaptic dual innervation, combining sensory inputs with top-down signals. Subclasses of layer 5 pyramidal neurons integrated these synapses with distinct temporal dynamics. Specifically, regular spiking cells exhibited supralinear enhancement of delayed-but not coincident-inputs, while intrinsic burst-firing neurons selectively boosted coincident synaptic events. These subthreshold integration characteristics translated to a nonlinear summation of action potential firing. Complementing electrophysiology with computational modeling, we found that distinct integration profiles arose from a cell-type-specific interaction of ionic mechanisms and feedforward inhibition. These data provide insight into the cellular properties that guide the nonlinear interaction of distinct long-range afferents in the neocortex.
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Affiliation(s)
- Daniel J Rindner
- Department of Neurobiology and Behavior, University of California, Irvine, 1215 McGaugh Hall, Irvine, CA 92697, USA
| | - Archana Proddutur
- Department of Neurobiology and Behavior, University of California, Irvine, 1215 McGaugh Hall, Irvine, CA 92697, USA
| | - Gyorgy Lur
- Department of Neurobiology and Behavior, University of California, Irvine, 1215 McGaugh Hall, Irvine, CA 92697, USA.
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13
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Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci 2022; 23:551-567. [PMID: 35732917 DOI: 10.1038/s41583-022-00606-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 12/17/2022]
Abstract
The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.
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Affiliation(s)
- Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany. .,Istituto Italiano di Tecnologia, Rovereto, Italy.
| | | | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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14
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Moore JJ, Robert V, Rashid SK, Basu J. Assessing Local and Branch-specific Activity in Dendrites. Neuroscience 2022; 489:143-164. [PMID: 34756987 PMCID: PMC9125998 DOI: 10.1016/j.neuroscience.2021.10.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 10/09/2021] [Accepted: 10/21/2021] [Indexed: 01/12/2023]
Abstract
Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.
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Affiliation(s)
- Jason J Moore
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Vincent Robert
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Shannon K Rashid
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Jayeeta Basu
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA.
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15
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Rolotti SV, Blockus H, Sparks FT, Priestley JB, Losonczy A. Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning. Neuron 2022; 110:977-991.e4. [PMID: 35041805 PMCID: PMC8930454 DOI: 10.1016/j.neuron.2021.12.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/23/2021] [Accepted: 12/10/2021] [Indexed: 12/17/2022]
Abstract
The hippocampus plays a critical role in memory consolidation, mediated by coordinated network activity during sharp-wave ripple (SWR) events. Despite the link between SWRs and hippocampal plasticity, little is known about how network state affects information processing in dendrites, the primary sites of synaptic input integration and plasticity. Here, we monitored somatic and basal dendritic activity in CA1 pyramidal cells in behaving mice using longitudinal two-photon calcium imaging integrated with simultaneous local field potential recordings. We found immobility was associated with an increase in dendritic activity concentrated during SWRs. Coincident dendritic and somatic activity during SWRs predicted increased coupling during subsequent exploration of a novel environment. In contrast, somatic-dendritic coupling and SWR recruitment varied with cells' tuning distance to reward location during a goal-learning task. Our results connect SWRs with the stabilization of information processing within CA1 neurons and suggest that these mechanisms may be dynamically biased by behavioral demands.
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Affiliation(s)
- Sebi V Rolotti
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Heike Blockus
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - James B Priestley
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
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16
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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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17
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Larkum ME, Wu J, Duverdin SA, Gidon A. The guide to dendritic spikes of the mammalian cortex in vitro and in vivo. Neuroscience 2022; 489:15-33. [PMID: 35182699 DOI: 10.1016/j.neuroscience.2022.02.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 02/01/2022] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.
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Affiliation(s)
- Matthew E Larkum
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Cluster, Charité - Universitätsmedizin Berlin, Germany
| | - Jiameng Wu
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Sarah A Duverdin
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Integrative Neurophysiology, Amsterdam Neuroscience, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Albert Gidon
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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18
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Lafourcade M, van der Goes MSH, Vardalaki D, Brown NJ, Voigts J, Yun DH, Kim ME, Ku T, Harnett MT. Differential dendritic integration of long-range inputs in association cortex via subcellular changes in synaptic AMPA-to-NMDA receptor ratio. Neuron 2022; 110:1532-1546.e4. [PMID: 35180389 PMCID: PMC9081173 DOI: 10.1016/j.neuron.2022.01.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/04/2021] [Accepted: 01/21/2022] [Indexed: 12/21/2022]
Abstract
Synaptic NMDA receptors can produce powerful dendritic supralinearities that expand the computational repertoire of single neurons and their respective circuits. This form of supralinearity may represent a general principle for synaptic integration in thin dendrites. However, individual cortical neurons receive many diverse classes of input that may require distinct postsynaptic decoding schemes. Here, we show that sensory, motor, and thalamic inputs preferentially target basal, apical oblique, and distal tuft dendrites, respectively, in layer 5b pyramidal neurons of the mouse retrosplenial cortex, a visuospatial association area. These dendritic compartments exhibited differential expression of NMDA receptor-mediated supralinearity due to systematic changes in the AMPA-to-NMDA receptor ratio. Our results reveal a new schema for integration in cortical pyramidal neurons, in which dendrite-specific changes in synaptic receptors support input-localized decoding. This coexistence of multiple modes of dendritic integration in single neurons has important implications for synaptic plasticity and cortical computation.
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Affiliation(s)
- Mathieu Lafourcade
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Marie-Sophie H van der Goes
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dimitra Vardalaki
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Norma J Brown
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jakob Voigts
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dae Hee Yun
- Department of Brain & Cognitive Sciences, Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Minyoung E Kim
- Department of Brain & Cognitive Sciences, Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Taeyun Ku
- Department of Brain & Cognitive Sciences, Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Mark T Harnett
- Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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19
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Magó Á, Kis N, Lükő B, Makara JK. Distinct dendritic Ca 2+ spike forms produce opposing input-output transformations in rat CA3 pyramidal cells. eLife 2021; 10:74493. [PMID: 34817378 PMCID: PMC8612760 DOI: 10.7554/elife.74493] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 10/29/2021] [Indexed: 01/09/2023] Open
Abstract
Proper integration of different inputs targeting the dendritic tree of CA3 pyramidal cells (CA3PCs) is critical for associative learning and recall. Dendritic Ca2+ spikes have been proposed to perform associative computations in other PC types by detecting conjunctive activation of different afferent input pathways, initiating afterdepolarization (ADP), and triggering burst firing. Implementation of such operations fundamentally depends on the actual biophysical properties of dendritic Ca2+ spikes; yet little is known about these properties in dendrites of CA3PCs. Using dendritic patch-clamp recordings and two-photon Ca2+ imaging in acute slices from male rats, we report that, unlike CA1PCs, distal apical trunk dendrites of CA3PCs exhibit distinct forms of dendritic Ca2+ spikes. Besides ADP-type global Ca2+ spikes, a majority of dendrites expresses a novel, fast Ca2+ spike type that is initiated locally without bAPs, can recruit additional Na+ currents, and is compartmentalized to the activated dendritic subtree. Occurrence of the different Ca2+ spike types correlates with dendritic structure, indicating morpho-functional heterogeneity among CA3PCs. Importantly, ADPs and dendritically initiated spikes produce opposing somatic output: bursts versus strictly single-action potentials, respectively. The uncovered variability of dendritic Ca2+ spikes may underlie heterogeneous input-output transformation and bursting properties of CA3PCs, and might specifically contribute to key associative and non-associative computations performed by the CA3 network.
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Affiliation(s)
- Ádám Magó
- Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary
| | - Noémi Kis
- Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary.,János Szentágothai School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Balázs Lükő
- Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary
| | - Judit K Makara
- Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Budapest, Hungary
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20
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Kourosh-Arami M, Hosseini N, Komaki A. Brain is modulated by neuronal plasticity during postnatal development. J Physiol Sci 2021; 71:34. [PMID: 34789147 PMCID: PMC10716960 DOI: 10.1186/s12576-021-00819-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 10/27/2021] [Indexed: 11/10/2022]
Abstract
Neuroplasticity is referred to the ability of the nervous system to change its structure or functions as a result of former stimuli. It is a plausible mechanism underlying a dynamic brain through adaptation processes of neural structure and activity patterns. Nevertheless, it is still unclear how the plastic neural systems achieve and maintain their equilibrium. Additionally, the alterations of balanced brain dynamics under different plasticity rules have not been explored either. Therefore, the present article primarily aims to review recent research studies regarding homosynaptic and heterosynaptic neuroplasticity characterized by the manipulation of excitatory and inhibitory synaptic inputs. Moreover, it attempts to understand different mechanisms related to the main forms of synaptic plasticity at the excitatory and inhibitory synapses during the brain development processes. Hence, this study comprised surveying those articles published since 1988 and available through PubMed, Google Scholar and science direct databases on a keyword-based search paradigm. All in all, the study results presented extensive and corroborative pieces of evidence for the main types of plasticity, including the long-term potentiation (LTP) and long-term depression (LTD) of the excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs).
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Affiliation(s)
- Masoumeh Kourosh-Arami
- Department of Neuroscience, School of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Nasrin Hosseini
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Komaki
- Neurophysiology Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
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21
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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: 23] [Impact Index Per Article: 7.7] [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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22
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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: 11] [Impact Index Per Article: 3.7] [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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23
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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: 4] [Impact Index Per Article: 1.3] [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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24
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Sutton NM, Ascoli GA. Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories. COGN SYST RES 2021; 70:80-92. [PMID: 34504394 DOI: 10.1016/j.cogsys.2021.07.008] [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] [Indexed: 11/26/2022]
Abstract
Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.
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Affiliation(s)
- Nate M Sutton
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
| | - Giorgio A Ascoli
- Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA).,Interdepartmental Neuroscience Program, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA)
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25
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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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26
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Stuyt G, Godenzini L, Palmer LM. Local and Global Dynamics of Dendritic Activity in the Pyramidal Neuron. Neuroscience 2021; 489:176-184. [PMID: 34280492 DOI: 10.1016/j.neuroscience.2021.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 12/22/2022]
Abstract
There has been increasing interest in the measurement and comparison of activity across compartments of the pyramidal neuron. Dendritic activity can occur both locally, on a single dendritic segment, or globally, involving multiple compartments of the single neuron. Little is known about how these dendritic dynamics shape and contribute to information processing and behavior. Although it has been difficult to characterize local and global activity in vivo due to the technical challenge of simultaneously recording from the entire dendritic arbor and soma, the rise of calcium imaging has driven the increased feasibility and interest of these experiments. However, the distinction between local and global activity made by calcium imaging requires careful consideration. In this review we describe local and global activity, discuss the difficulties and caveats of this distinction, and present the evidence of local and global activity in information processing and behavior.
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Affiliation(s)
- George Stuyt
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Luca Godenzini
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Lucy M Palmer
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia.
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27
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Adoff MD, Climer JR, Davoudi H, Marvin JS, Looger LL, Dombeck DA. The functional organization of excitatory synaptic input to place cells. Nat Commun 2021; 12:3558. [PMID: 34117238 PMCID: PMC8196201 DOI: 10.1038/s41467-021-23829-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/19/2021] [Indexed: 12/23/2022] Open
Abstract
Hippocampal place cells contribute to mammalian spatial navigation and memory formation. Numerous models have been proposed to explain the location-specific firing of this cognitive representation, but the pattern of excitatory synaptic input leading to place firing is unknown, leaving no synaptic-scale explanation of place coding. Here we used resonant scanning two-photon microscopy to establish the pattern of synaptic glutamate input received by CA1 place cells in behaving mice. During traversals of the somatic place field, we found increased excitatory dendritic input, mainly arising from inputs with spatial tuning overlapping the somatic field, and functional clustering of this input along the dendrites over ~10 µm. These results implicate increases in total excitatory input and co-activation of anatomically clustered synaptic input in place firing. Since they largely inherit their fields from upstream synaptic partners with similar fields, many CA1 place cells appear to be part of multi-brain-region cell assemblies forming representations of specific locations.
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Affiliation(s)
- Michael D Adoff
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Jason R Climer
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Heydar Davoudi
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL, USA.
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28
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Chavlis S, Poirazi P. Drawing inspiration from biological dendrites to empower artificial neural networks. Curr Opin Neurobiol 2021; 70:1-10. [PMID: 34087540 DOI: 10.1016/j.conb.2021.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/21/2021] [Accepted: 04/28/2021] [Indexed: 12/24/2022]
Abstract
This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.
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Affiliation(s)
- Spyridon Chavlis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 70013, Greece
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 70013, Greece.
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29
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Gandolfi D, Boiani GM, Bigiani A, Mapelli J. Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders. Int J Mol Sci 2021; 22:4565. [PMID: 33925434 PMCID: PMC8123833 DOI: 10.3390/ijms22094565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
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30
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Sáray S, Rössert CA, Appukuttan S, Migliore R, Vitale P, Lupascu CA, Bologna LL, Van Geit W, Romani A, Davison AP, Muller E, Freund TF, Káli S. HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLoS Comput Biol 2021; 17:e1008114. [PMID: 33513130 PMCID: PMC7875359 DOI: 10.1371/journal.pcbi.1008114] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 02/10/2021] [Accepted: 12/24/2020] [Indexed: 11/19/2022] Open
Abstract
Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.
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Affiliation(s)
- Sára Sáray
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Shailesh Appukuttan
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Paola Vitale
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, Canada
- CHU Sainte-Justine Research Center, Montreal, Canada
- Quebec Artificial Intelligence Institute (Mila), Montreal, Canada
| | - Tamás F. Freund
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
| | - Szabolcs Káli
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
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31
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Bick C, Goodfellow M, Laing CR, Martens EA. Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2020; 10:9. [PMID: 32462281 PMCID: PMC7253574 DOI: 10.1186/s13408-020-00086-9] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 05/07/2020] [Indexed: 05/03/2023]
Abstract
Many biological and neural systems can be seen as networks of interacting periodic processes. Importantly, their functionality, i.e., whether these networks can perform their function or not, depends on the emerging collective dynamics of the network. Synchrony of oscillations is one of the most prominent examples of such collective behavior and has been associated both with function and dysfunction. Understanding how network structure and interactions, as well as the microscopic properties of individual units, shape the emerging collective dynamics is critical to find factors that lead to malfunction. However, many biological systems such as the brain consist of a large number of dynamical units. Hence, their analysis has either relied on simplified heuristic models on a coarse scale, or the analysis comes at a huge computational cost. Here we review recently introduced approaches, known as the Ott-Antonsen and Watanabe-Strogatz reductions, allowing one to simplify the analysis by bridging small and large scales. Thus, reduced model equations are obtained that exactly describe the collective dynamics for each subpopulation in the oscillator network via few collective variables only. The resulting equations are next-generation models: Rather than being heuristic, they exactly link microscopic and macroscopic descriptions and therefore accurately capture microscopic properties of the underlying system. At the same time, they are sufficiently simple to analyze without great computational effort. In the last decade, these reduction methods have become instrumental in understanding how network structure and interactions shape the collective dynamics and the emergence of synchrony. We review this progress based on concrete examples and outline possible limitations. Finally, we discuss how linking the reduced models with experimental data can guide the way towards the development of new treatment approaches, for example, for neurological disease.
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Affiliation(s)
- Christian Bick
- Centre for Systems, Dynamics, and Control, University of Exeter, Exeter, UK.
- Department of Mathematics, University of Exeter, Exeter, UK.
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.
- Mathematical Institute, University of Oxford, Oxford, UK.
- Institute for Advanced Study, Technische Universität München, Garching, Germany.
| | - Marc Goodfellow
- Department of Mathematics, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK
| | - Carlo R Laing
- School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
| | - Erik A Martens
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.
- Department of Biomedical Science, University of Copenhagen, Copenhagen N, Denmark.
- Centre for Translational Neuroscience, University of Copenhagen, Copenhagen N, Denmark.
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32
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Poirazi P, Papoutsi A. Illuminating dendritic function with computational models. Nat Rev Neurosci 2020; 21:303-321. [PMID: 32393820 DOI: 10.1038/s41583-020-0301-7] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2020] [Indexed: 02/06/2023]
Abstract
Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.
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Affiliation(s)
- Panayiota Poirazi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
| | - Athanasia Papoutsi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece
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33
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Kastellakis G, Poirazi P. Synaptic Clustering and Memory Formation. Front Mol Neurosci 2019; 12:300. [PMID: 31866824 PMCID: PMC6908852 DOI: 10.3389/fnmol.2019.00300] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 01/12/2023] Open
Abstract
In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed "synapse clustering" and has been shown to emerge in many different memory-related paradigms, as well as in in vitro studies. The clustering of synapses may emerge from synapses receiving similar input, or via many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.
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Affiliation(s)
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece
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34
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Kerlin A, Mohar B, Flickinger D, MacLennan BJ, Dean MB, Davis C, Spruston N, Svoboda K. Functional clustering of dendritic activity during decision-making. eLife 2019; 8:46966. [PMID: 31663507 PMCID: PMC6821494 DOI: 10.7554/elife.46966] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 09/26/2019] [Indexed: 01/08/2023] Open
Abstract
The active properties of dendrites can support local nonlinear operations, but previous imaging and electrophysiological measurements have produced conflicting views regarding the prevalence and selectivity of local nonlinearities in vivo. We imaged calcium signals in pyramidal cell dendrites in the motor cortex of mice performing a tactile decision task. A custom microscope allowed us to image the soma and up to 300 μm of contiguous dendrite at 15 Hz, while resolving individual spines. New analysis methods were used to estimate the frequency and spatial scales of activity in dendritic branches and spines. The majority of dendritic calcium transients were coincident with global events. However, task-associated calcium signals in dendrites and spines were compartmentalized by dendritic branching and clustered within branches over approximately 10 μm. Diverse behavior-related signals were intermingled and distributed throughout the dendritic arbor, potentially supporting a large learning capacity in individual neurons.
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Affiliation(s)
- Aaron Kerlin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Boaz Mohar
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Daniel Flickinger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Bryan J MacLennan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Matthew B Dean
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Courtney Davis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
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35
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Abstract
Axons functionally link the somato-dendritic compartment to synaptic terminals. Structurally and functionally diverse, they accomplish a central role in determining the delays and reliability with which neuronal ensembles communicate. By combining their active and passive biophysical properties, they ensure a plethora of physiological computations. In this review, we revisit the biophysics of generation and propagation of electrical signals in the axon and their dynamics. We further place the computational abilities of axons in the context of intracellular and intercellular coupling. We discuss how, by means of sophisticated biophysical mechanisms, axons expand the repertoire of axonal computation, and thereby, of neural computation.
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Affiliation(s)
- Pepe Alcami
- Division of Neurobiology, Department of Biology II, Ludwig-Maximilians-Universitaet Muenchen, Martinsried, Germany
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Ahmed El Hady
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ, United States
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36
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Weissenberger F, Gauy MM, Zou X, Steger A. Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction. Neural Comput 2019; 31:2252-2265. [PMID: 31525311 DOI: 10.1162/neco_a_01230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.
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Affiliation(s)
- Felix Weissenberger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich CH-8092, Switzerland
| | - Marcelo Matheus Gauy
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich CH-8092, Switzerland
| | - Xun Zou
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich CH-8092, Switzerland
| | - Angelika Steger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich CH-8092, Switzerland
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37
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The Autism-Associated Gene Scn2a Contributes to Dendritic Excitability and Synaptic Function in the Prefrontal Cortex. Neuron 2019; 103:673-685.e5. [PMID: 31230762 DOI: 10.1016/j.neuron.2019.05.037] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 03/23/2019] [Accepted: 05/22/2019] [Indexed: 12/20/2022]
Abstract
Autism spectrum disorder (ASD) is strongly associated with de novo gene mutations. One of the most commonly affected genes is SCN2A. ASD-associated SCN2A mutations impair the encoded protein NaV1.2, a sodium channel important for action potential initiation and propagation in developing excitatory cortical neurons. The link between an axonal sodium channel and ASD, a disorder typically attributed to synaptic or transcriptional dysfunction, is unclear. Here we show that NaV1.2 is unexpectedly critical for dendritic excitability and synaptic function in mature pyramidal neurons in addition to regulating early developmental axonal excitability. NaV1.2 loss reduced action potential backpropagation into dendrites, impairing synaptic plasticity and synaptic strength, even when NaV1.2 expression was disrupted in a cell-autonomous fashion late in development. These results reveal a novel dendritic function for NaV1.2, providing insight into cellular mechanisms probably underlying circuit and behavioral dysfunction in ASD.
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38
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Francavilla R, Villette V, Martel O, Topolnik L. Calcium Dynamics in Dendrites of Hippocampal CA1 Interneurons in Awake Mice. Front Cell Neurosci 2019; 13:98. [PMID: 30930750 PMCID: PMC6428725 DOI: 10.3389/fncel.2019.00098] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/27/2019] [Indexed: 11/19/2022] Open
Abstract
Hippocampal inhibitory interneurons exhibit a large diversity of dendritic Ca2+ mechanisms that are involved in the induction of Hebbian and anti-Hebbian synaptic plasticity. High resolution imaging techniques allowed examining somatic Ca2+ signals and, accordingly, the recruitment of hippocampal interneurons in awake behaving animals. However, little is still known about dendritic Ca2+ activity in interneurons during different behavioral states. Here, we used two-photon Ca2+ imaging in mouse hippocampal CA1 interneurons to reveal Ca2+ signal patterns in interneuron dendrites during animal locomotion and immobility. Despite overall variability in dendritic Ca2+ transients (CaTs) across different cells and dendritic branches, we report consistent behavior state-dependent organization of Ca2+ signaling in interneurons. As such, spreading regenerative CaTs dominated in dendrites during locomotion, whereas both spreading and localized Ca2+ signals were seen during immobility. Thus, these data indicate that while animal locomotion is associated with widespread Ca2+ elevations in interneuron dendrites that may reflect regenerative activity, local CaTs that may be related to synaptic activity become apparent during animal quiet state.
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Affiliation(s)
- Ruggiero Francavilla
- Department of Biochemistry, Microbiology and Bio-informatics, Faculty of Science and Engineering, Neuroscience Axis, CHU de Québec Research Center (CHUL), Laval University, Québec, PQ, Canada
| | - Vincent Villette
- Department of Biochemistry, Microbiology and Bio-informatics, Faculty of Science and Engineering, Neuroscience Axis, CHU de Québec Research Center (CHUL), Laval University, Québec, PQ, Canada
| | - Olivier Martel
- Department of Biochemistry, Microbiology and Bio-informatics, Faculty of Science and Engineering, Neuroscience Axis, CHU de Québec Research Center (CHUL), Laval University, Québec, PQ, Canada
| | - Lisa Topolnik
- Department of Biochemistry, Microbiology and Bio-informatics, Faculty of Science and Engineering, Neuroscience Axis, CHU de Québec Research Center (CHUL), Laval University, Québec, PQ, Canada
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39
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Sheffield ME, Dombeck DA. Dendritic mechanisms of hippocampal place field formation. Curr Opin Neurobiol 2019; 54:1-11. [PMID: 30036841 DOI: 10.1016/j.conb.2018.07.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 06/18/2018] [Accepted: 07/06/2018] [Indexed: 10/28/2022]
Abstract
Place cells in the hippocampus are thought to form a cognitive map of space and a memory of places. How this map forms when animals are exposed to novel environments has been the subject of a great deal of research. Numerous technical advances over the past decade greatly increased our understanding of the precise mechanisms underlying place field formation. In particular, it is now possible to connect cellular and circuit mechanisms of integration, firing, and plasticity discovered in brain slices, to processes taking place in vivo as animals learn and encode novel environments. Here, we focus on recent results and describe the dendritic mechanisms most likely responsible for the formation of place fields. We also discuss key open questions that are likely to be answered in the coming years.
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Affiliation(s)
- Mark Ej Sheffield
- Department of Neurobiology, Grossman Institute for Neuroscience, The University of Chicago, Chicago, IL 60637, USA.
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL 60201, USA.
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40
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Kumar A, Schiff O, Barkai E, Mel BW, Poleg-Polsky A, Schiller J. NMDA spikes mediate amplification of inputs in the rat piriform cortex. eLife 2018; 7:38446. [PMID: 30575520 PMCID: PMC6333441 DOI: 10.7554/elife.38446] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 12/20/2018] [Indexed: 11/13/2022] Open
Abstract
The piriform cortex (PCx) receives direct input from the olfactory bulb (OB) and is the brain's main station for odor recognition and memory. The transformation of the odor code from OB to PCx is profound: mitral and tufted cells in olfactory glomeruli respond to individual odorant molecules, whereas pyramidal neurons (PNs) in the PCx responds to multiple, apparently random combinations of activated glomeruli. How these 'discontinuous' receptive fields are formed from OB inputs remains unknown. Counter to the prevailing view that olfactory PNs sum their inputs passively, we show for the first time that NMDA spikes within individual dendrites can both amplify OB inputs and impose combination selectivity upon them, while their ability to compartmentalize voltage signals allows different dendrites to represent different odorant combinations. Thus, the 2-layer integrative behavior of olfactory PN dendrites provides a parsimonious account for the nonlinear remapping of the odor code from bulb to cortex.
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Affiliation(s)
- Amit Kumar
- Department of Physiology, The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Oded Schiff
- Department of Physiology, The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Edi Barkai
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Bartlett W Mel
- Biomedical Engineering Department, University of Southern California, Los Angeles, United States
| | - Alon Poleg-Polsky
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, United States
| | - Jackie Schiller
- Department of Physiology, The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa, Israel
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41
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Matheus Gauy M, Lengler J, Einarsson H, Meier F, Weissenberger F, Yanik MF, Steger A. A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences. Front Neurosci 2018; 12:961. [PMID: 30618583 PMCID: PMC6306028 DOI: 10.3389/fnins.2018.00961] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 12/03/2018] [Indexed: 01/09/2023] Open
Abstract
The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.
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Affiliation(s)
- Marcelo Matheus Gauy
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Johannes Lengler
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Hafsteinn Einarsson
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Florian Meier
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Felix Weissenberger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
| | - Mehmet Fatih Yanik
- Department of Information Technology and Electrical Engineering, Institute for Neuroinformatics, ETH Zurich, Zurich, Switzerland
| | - Angelika Steger
- Department of Computer Science, Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland
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42
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Takahashi N. Synaptic topography - Converging connections and emerging function. Neurosci Res 2018; 141:29-35. [PMID: 30468748 DOI: 10.1016/j.neures.2018.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 10/16/2018] [Accepted: 11/01/2018] [Indexed: 11/25/2022]
Abstract
Brain circuits are constituted of individual neurons that are interconnected with a vast array of synapses. In order to understand how brain function emerges from this complex synaptic network, immense efforts have been made to trace the synaptic topography, i.e. arrangement of synaptic connections, of the network. In addition to anatomically elaborating the synaptic layout at multiple levels across brain regions, recent studies have attempted to elucidate the fundamental wiring principles that govern neural information processing in the brain, establishing a link between anatomy and function. In this review, I will discuss recent discoveries on the topographical organization of synaptic connections at the cell-to-cell and subcellular levels in the cortex and hippocampus. Accumulating evidence leads us to acknowledge the highly structured, non-random synaptic connectivity that emerges together with sensory feature preferences of neurons and synchronous neuronal activity.
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Affiliation(s)
- Naoya Takahashi
- Institute for Biology, Neuronal Plasticity, Humboldt University of Berlin, D-10117, Berlin, Germany.
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Grillo FW, Neves G, Walker A, Vizcay-Barrena G, Fleck RA, Branco T, Burrone J. A Distance-Dependent Distribution of Presynaptic Boutons Tunes Frequency-Dependent Dendritic Integration. Neuron 2018; 99:275-282.e3. [PMID: 29983327 PMCID: PMC6078905 DOI: 10.1016/j.neuron.2018.06.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 03/23/2018] [Accepted: 06/08/2018] [Indexed: 11/28/2022]
Abstract
How presynaptic inputs and neurotransmitter release dynamics are distributed along a dendritic tree is not well established. Here, we show that presynaptic boutons that form onto basal dendrites of CA1 pyramidal neurons display a decrease in active zone (AZ) size with distance from the soma, resulting in a distance-dependent increase in short-term facilitation. Our findings suggest that the spatial distribution of short-term facilitation serves to compensate for the electrotonic attenuation of subthreshold distal inputs during repeated stimulation and fine-tunes the preferred input frequency of dendritic domains. Presynaptic inputs decrease in size with distance along CA1 basal dendrites Release probability decreases with distance along basal dendrites Short-term facilitation increases with distance along basal dendrites Increased synaptic facilitation offsets passive decay and boosts supralinear events
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Affiliation(s)
- Federico W Grillo
- Centre for Developmental Neurobiology, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK; MRC Centre for Neurodevelopmental Disorders, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK
| | - Guilherme Neves
- Centre for Developmental Neurobiology, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK; MRC Centre for Neurodevelopmental Disorders, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK
| | - Alison Walker
- Centre for Developmental Neurobiology, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK
| | - Gema Vizcay-Barrena
- Centre for Ultrastructural Imaging (CUI), Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK
| | - Roland A Fleck
- Centre for Ultrastructural Imaging (CUI), Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK
| | - Tiago Branco
- The Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Juan Burrone
- Centre for Developmental Neurobiology, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK; MRC Centre for Neurodevelopmental Disorders, Kings College London, New Hunts House, Guys Hospital Campus, London, SE1 1UL, UK.
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44
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Antic SD, Hines M, Lytton WW. Embedded ensemble encoding hypothesis: The role of the "Prepared" cell. J Neurosci Res 2018; 96:1543-1559. [PMID: 29633330 DOI: 10.1002/jnr.24240] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 03/10/2018] [Accepted: 03/12/2018] [Indexed: 01/08/2023]
Abstract
We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.
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Affiliation(s)
- Srdjan D Antic
- Department of Neuroscience, Institute for Systems Genomics, Stem Cell Institute, UConn Health, Farmington, Connecticut
| | - Michael Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - William W Lytton
- Physiology and Pharmacology, Neurology, Biomedical Engineering, SUNY Downstate Medical Center, Brooklyn, New York.,Department of Neurology, Kings County Hospital, Brooklyn, New York
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45
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Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites. Nat Neurosci 2018; 21:353-363. [PMID: 29459763 DOI: 10.1038/s41593-018-0084-6] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 01/05/2018] [Indexed: 01/05/2023]
Abstract
CA1 pyramidal neurons are a major output of the hippocampus and encode features of experience that constitute episodic memories. Feature-selective firing of these neurons results from the dendritic integration of inputs from multiple brain regions. While it is known that synchronous activation of spatially clustered inputs can contribute to firing through the generation of dendritic spikes, there is no established mechanism for spatiotemporal synaptic clustering. Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. Our results suggest that distinct afferent projections use different connectivity motifs that differentially contribute to dendritic integration.
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46
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Bono J, Wilmes KA, Clopath C. Modelling plasticity in dendrites: from single cells to networks. Curr Opin Neurobiol 2017; 46:136-141. [PMID: 28888857 DOI: 10.1016/j.conb.2017.08.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 08/23/2017] [Indexed: 02/06/2023]
Abstract
One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.
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Affiliation(s)
- Jacopo Bono
- Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Katharina A Wilmes
- Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
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47
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Kalmbach BE, Gray R, Johnston D, Cook EP. Systems-based analysis of dendritic nonlinearities reveals temporal feature extraction in mouse L5 cortical neurons. J Neurophysiol 2017; 117:2188-2208. [PMID: 28250154 DOI: 10.1152/jn.00951.2016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 02/14/2017] [Accepted: 02/27/2017] [Indexed: 01/29/2023] Open
Abstract
What do dendritic nonlinearities tell a neuron about signals injected into the dendrite? Linear and nonlinear dendritic components affect how time-varying inputs are transformed into action potentials (APs), but the relative contribution of each component is unclear. We developed a novel systems-identification approach to isolate the nonlinear response of layer 5 pyramidal neuron dendrites in mouse prefrontal cortex in response to dendritic current injections. We then quantified the nonlinear component and its effect on the soma, using functional models composed of linear filters and static nonlinearities. Both noise and waveform current injections revealed linear and nonlinear components in the dendritic response. The nonlinear component consisted of fast Na+ spikes that varied in amplitude 10-fold in a single neuron. A functional model reproduced the timing and amplitude of the dendritic spikes and revealed that they were selective to a preferred input dynamic (~4.5 ms rise time). The selectivity of the dendritic spikes became wider in the presence of additive noise, which was also predicted by the functional model. A second functional model revealed that the dendritic spikes were weakly boosted before being linearly integrated at the soma. For both our noise and waveform dendritic input, somatic APs were dependent on the somatic integration of the stimulus, followed a subset of large dendritic spikes, and were selective to the same input dynamics preferred by the dendrites. Our results suggest that the amplitude of fast dendritic spikes conveys information about high-frequency features in the dendritic input, which is then combined with low-frequency somatic integration.NEW & NOTEWORTHY The nonlinear response of layer 5 mouse pyramidal dendrites was isolated with a novel systems-based approach. In response to dendritic current injections, the nonlinear component contained mostly fast, variable-amplitude, Na+ spikes. A functional model accounted for the timing and amplitude of the dendritic spikes and revealed that dendritic spikes are selective to a preferred input dynamic, which was verified experimentally. Thus, fast dendritic nonlinearities behave as high-frequency feature detectors that influence somatic action potentials.
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Affiliation(s)
- Brian E Kalmbach
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas; and
| | - Richard Gray
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas; and
| | - Daniel Johnston
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas; and
| | - Erik P Cook
- Centre for Mathematics in Bioscience and Medicine, Department of Physiology, McGill University, Montreal, Quebec, Canada
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Manita S, Miyakawa H, Kitamura K, Murayama M. Dendritic Spikes in Sensory Perception. Front Cell Neurosci 2017; 11:29. [PMID: 28261060 PMCID: PMC5309249 DOI: 10.3389/fncel.2017.00029] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 01/31/2017] [Indexed: 11/13/2022] Open
Abstract
What is the function of dendritic spikes? One might argue that they provide conditions for neuronal plasticity or that they are essential for neural computation. However, despite a long history of dendritic research, the physiological relevance of dendritic spikes in brain function remains unknown. This could stem from the fact that most studies on dendrites have been performed in vitro. Fortunately, the emergence of novel techniques such as improved two-photon microscopy, genetically encoded calcium indicators (GECIs), and optogenetic tools has provided the means for vital breakthroughs in in vivo dendritic research. These technologies enable the investigation of the functions of dendritic spikes in behaving animals, and thus, help uncover the causal relationship between dendritic spikes, and sensory information processing and synaptic plasticity. Understanding the roles of dendritic spikes in brain function would provide mechanistic insight into the relationship between the brain and the mind. In this review article, we summarize the results of studies on dendritic spikes from a historical perspective and discuss the recent advances in our understanding of the role of dendritic spikes in sensory perception.
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Affiliation(s)
- Satoshi Manita
- Laboratory for Behavioral Neurophysiology, Brain Science Institute, RIKENWako City, Saitama, Japan; Department of Neurophysiology, Division of Medicine, University of YamanashiChuo-shi, Japan
| | - Hiroyoshi Miyakawa
- Laboratory of Cellular Neurobiology, Tokyo University of Pharmacy and Life Sciences Hachioji, Japan
| | - Kazuo Kitamura
- Department of Neurophysiology, Division of Medicine, University of Yamanashi Chuo-shi, Japan
| | - Masanori Murayama
- Laboratory for Behavioral Neurophysiology, Brain Science Institute, RIKEN Wako City, Saitama, Japan
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Singer W, Lazar A. Does the Cerebral Cortex Exploit High-Dimensional, Non-linear Dynamics for Information Processing? Front Comput Neurosci 2016; 10:99. [PMID: 27713697 PMCID: PMC5031693 DOI: 10.3389/fncom.2016.00099] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 09/02/2016] [Indexed: 12/04/2022] Open
Abstract
The discovery of stimulus induced synchronization in the visual cortex suggested the possibility that the relations among low-level stimulus features are encoded by the temporal relationship between neuronal discharges. In this framework, temporal coherence is considered a signature of perceptual grouping. This insight triggered a large number of experimental studies which sought to investigate the relationship between temporal coordination and cognitive functions. While some core predictions derived from the initial hypothesis were confirmed, these studies, also revealed a rich dynamical landscape beyond simple coherence whose role in signal processing is still poorly understood. In this paper, a framework is presented which establishes links between the various manifestations of cortical dynamics by assigning specific coding functions to low-dimensional dynamic features such as synchronized oscillations and phase shifts on the one hand and high-dimensional non-linear, non-stationary dynamics on the other. The data serving as basis for this synthetic approach have been obtained with chronic multisite recordings from the visual cortex of anesthetized cats and from monkeys trained to solve cognitive tasks. It is proposed that the low-dimensional dynamics characterized by synchronized oscillations and large-scale correlations are substates that represent the results of computations performed in the high-dimensional state-space provided by recurrently coupled networks.
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Affiliation(s)
- Wolf Singer
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck SocietyFrankfurt am Main, Germany; Max Planck Institute for Brain ResearchFrankfurt am Main, Germany; Frankfurt Institute for Advanced StudiesFrankfurt am Main, Germany
| | - Andreea Lazar
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck SocietyFrankfurt am Main, Germany; Max Planck Institute for Brain ResearchFrankfurt am Main, Germany; Frankfurt Institute for Advanced StudiesFrankfurt am Main, Germany
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50
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Almog M, Korngreen A. Is realistic neuronal modeling realistic? J Neurophysiol 2016; 116:2180-2209. [PMID: 27535372 DOI: 10.1152/jn.00360.2016] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/17/2016] [Indexed: 11/22/2022] Open
Abstract
Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.
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Affiliation(s)
- Mara Almog
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and.,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Alon Korngreen
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and .,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
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