1
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Glausier JR, Bouchet-Marquis C, Maier M, Banks-Tibbs T, Wu K, Ning J, Melchitzky D, Lewis DA, Freyberg Z. Volume electron microscopy reveals 3D synaptic nanoarchitecture in postmortem human prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582174. [PMID: 38463986 PMCID: PMC10925168 DOI: 10.1101/2024.02.26.582174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Synaptic function is directly reflected in quantifiable ultrastructural features using electron microscopy (EM) approaches. This coupling of synaptic function and ultrastructure suggests that in vivo synaptic function can be inferred from EM analysis of ex vivo human brain tissue. To investigate this, we employed focused ion beam-scanning electron microscopy (FIB-SEM), a volume EM (VEM) approach, to generate ultrafine-resolution, three-dimensional (3D) micrographic datasets of postmortem human dorsolateral prefrontal cortex (DLPFC), a region with cytoarchitectonic characteristics distinct to human brain. Synaptic, sub-synaptic, and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, 3D neuropil reconstruction revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of heightened synaptic communication, integration, and plasticity. Altogether, our findings provide critical proof-of-concept data demonstrating that ex vivo VEM analysis is an effective approach to infer in vivo synaptic functioning in human brain.
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Affiliation(s)
- Jill R. Glausier
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | | | - Matthew Maier
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - Tabitha Banks-Tibbs
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA
- College of Medicine, The Ohio State University, Columbus, OH
| | - Ken Wu
- Materials and Structural Analysis, Thermo Fisher Scientific, Hillsboro, OR
| | - Jiying Ning
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | | | - David A. Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - Zachary Freyberg
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA
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2
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Yaeger CE, Vardalaki D, Zhang Q, Pham TLD, Brown NJ, Ji N, Harnett MT. A dendritic mechanism for balancing synaptic flexibility and stability. Cell Rep 2024; 43:114638. [PMID: 39167486 PMCID: PMC11403626 DOI: 10.1016/j.celrep.2024.114638] [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: 04/16/2024] [Revised: 06/28/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.
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Affiliation(s)
- Courtney E Yaeger
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dimitra Vardalaki
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Qinrong Zhang
- Department of Physics, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Trang L D Pham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Norma J Brown
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Mark T Harnett
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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3
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Fischer LF, Xu L, Murray KT, Harnett MT. Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.18.607457. [PMID: 39229229 PMCID: PMC11370392 DOI: 10.1101/2024.08.18.607457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.
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Affiliation(s)
- Lukas F Fischer
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Liane Xu
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Keith T Murray
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Mark T Harnett
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
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4
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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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5
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Morabito A, Zerlaut Y, Dhanasobhon D, Berthaux E, Tzilivaki A, Moneron G, Cathala L, Poirazi P, Bacci A, DiGregorio D, Lourenço J, Rebola N. A dendritic substrate for temporal diversity of cortical inhibition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602783. [PMID: 39026855 PMCID: PMC11257522 DOI: 10.1101/2024.07.09.602783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
In the mammalian neocortex, GABAergic interneurons (INs) inhibit cortical networks in profoundly different ways. The extent to which this depends on how different INs process excitatory signals along their dendrites is poorly understood. Here, we reveal that the functional specialization of two major populations of cortical INs is determined by the unique association of different dendritic integration modes with distinct synaptic organization motifs. We found that somatostatin (SST)-INs exhibit NMDAR-dependent dendritic integration and uniform synapse density along the dendritic tree. In contrast, dendrites of parvalbumin (PV)-INs exhibit passive synaptic integration coupled with proximally enriched synaptic distributions. Theoretical analysis shows that these two dendritic configurations result in different strategies to optimize synaptic efficacy in thin dendritic structures. Yet, the two configurations lead to distinct temporal engagement of each IN during network activity. We confirmed these predictions with in vivo recordings of IN activity in the visual cortex of awake mice, revealing a rapid and linear recruitment of PV-INs as opposed to a long-lasting integrative activation of SST-INs. Our work reveals the existence of distinct dendritic strategies that confer distinct temporal representations for the two major classes of neocortical INs and thus dynamics of inhibition.
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Affiliation(s)
- Annunziato Morabito
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Yann Zerlaut
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Dhanasak Dhanasobhon
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Emmanuelle Berthaux
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Alexandra Tzilivaki
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität zu Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117 Berlin, Germany
- Einstein Center for Neurosciences, Chariteplatz 1, 10117 Berlin, Germany
- NeuroCure Cluster of Excellence, Chariteplatz 1, 10117 Berlin, Germany
| | - Gael Moneron
- Institut Pasteur, CNRS UMR3571, Synapse and Circuit Dynamics Unit, 75015 Paris, France
| | - Laurence Cathala
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece
| | - Alberto Bacci
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - David DiGregorio
- Institut Pasteur, CNRS UMR3571, Synapse and Circuit Dynamics Unit, 75015 Paris, France
| | - Joana Lourenço
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
| | - Nelson Rebola
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Inserm, CNRS, Paris, 75013, France
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6
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Kim HH, Bonekamp KE, Gillie GR, Autio DM, Keller T, Crandall SR. Functional Dynamics and Selectivity of Two Parallel Corticocortical Pathways from Motor Cortex to Layer 5 Circuits in Somatosensory Cortex. eNeuro 2024; 11:ENEURO.0154-24.2024. [PMID: 38834298 PMCID: PMC11209671 DOI: 10.1523/eneuro.0154-24.2024] [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: 04/05/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
In the rodent whisker system, active sensing and sensorimotor integration are mediated in part by the dynamic interactions between the motor cortex (M1) and somatosensory cortex (S1). However, understanding these dynamic interactions requires knowledge about the synapses and how specific neurons respond to their input. Here, we combined optogenetics, retrograde labeling, and electrophysiology to characterize the synaptic connections between M1 and layer 5 (L5) intratelencephalic (IT) and pyramidal tract (PT) neurons in S1 of mice (both sexes). We found that M1 synapses onto IT cells displayed modest short-term depression, whereas synapses onto PT neurons showed robust short-term facilitation. Despite M1 inputs to IT cells depressing, their slower kinetics resulted in summation and a response that increased during short trains. In contrast, summation was minimal in PT neurons due to the fast time course of their M1 responses. The functional consequences of this reduced summation, however, were outweighed by the strong facilitation at these M1 synapses, resulting in larger response amplitudes in PT neurons than IT cells during repetitive stimulation. To understand the impact of facilitating M1 inputs on PT output, we paired trains of inputs with single backpropagating action potentials, finding that repetitive M1 activation increased the probability of bursts in PT cells without impacting the time dependence of this coupling. Thus, there are two parallel but dynamically distinct systems of M1 synaptic excitation in L5 of S1, each defined by the short-term dynamics of its synapses, the class of postsynaptic neurons, and how the neurons respond to those inputs.
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Affiliation(s)
- Hye-Hyun Kim
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
| | - Kelly E Bonekamp
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University, East Lansing, Michigan 48824
| | - Grant R Gillie
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University, East Lansing, Michigan 48824
| | - Dawn M Autio
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
| | - Tryton Keller
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
| | - Shane R Crandall
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University, East Lansing, Michigan 48824
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7
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Kim HH, Bonekamp KE, Gillie GR, Autio DM, Keller T, Crandall SR. Functional dynamics and selectivity of two parallel corticocortical pathways from motor cortex to layer 5 circuits in somatosensory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.11.579810. [PMID: 38405888 PMCID: PMC10888929 DOI: 10.1101/2024.02.11.579810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In the rodent whisker system, active sensing and sensorimotor integration are mediated in part by the dynamic interactions between the motor cortex (M1) and somatosensory cortex (S1). However, understanding these dynamic interactions requires knowledge about the synapses and how specific neurons respond to their input. Here, we combined optogenetics, retrograde labeling, and electrophysiology to characterize the synaptic connections between M1 and layer 5 (L5) intratelencephalic (IT) and pyramidal tract (PT) neurons in S1 of mice (both sexes). We found that M1 synapses onto IT cells displayed modest short-term depression, whereas synapses onto PT neurons showed robust short-term facilitation. Despite M1 inputs to IT cells depressing, their slower kinetics resulted in summation and a response that increased during short trains. In contrast, summation was minimal in PT neurons due to the fast time course of their M1 responses. The functional consequences of this reduced summation, however, were outweighed by the strong facilitation at these M1 synapses, resulting in larger response amplitudes in PT neurons than IT cells during repetitive stimulation. To understand the impact of facilitating M1 inputs on PT output, we paired trains of inputs with single backpropagating action potentials, finding that repetitive M1 activation increased the probability of bursts in PT cells without impacting the time-dependence of this coupling. Thus, there are two parallel but dynamically distinct systems of M1 synaptic excitation in L5 of S1, each defined by the short-term dynamics of its synapses, the class of postsynaptic neurons, and how the neurons respond to those inputs.
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Affiliation(s)
- Hye-Hyun Kim
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
| | - Kelly E. Bonekamp
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University East Lansing, MI 48824, USA
| | - Grant R. Gillie
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University East Lansing, MI 48824, USA
| | - Dawn M. Autio
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
| | - Tryton Keller
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
| | - Shane R. Crandall
- Department of Physiology, Michigan State University, East Lansing, MI 48824, USA
- Molecular, Cellular, and Integrative Physiology Program, Michigan State University East Lansing, MI 48824, USA
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8
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Fitz H, Hagoort P, Petersson KM. Neurobiological Causal Models of Language Processing. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:225-247. [PMID: 38645618 PMCID: PMC11025648 DOI: 10.1162/nol_a_00133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/18/2023] [Indexed: 04/23/2024]
Abstract
The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.
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Affiliation(s)
- Hartmut Fitz
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Peter Hagoort
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Karl Magnus Petersson
- Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Faculty of Medicine and Biomedical Sciences, University of Algarve, Faro, Portugal
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9
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Brake N, Duc F, Rokos A, Arseneau F, Shahiri S, Khadra A, Plourde G. A neurophysiological basis for aperiodic EEG and the background spectral trend. Nat Commun 2024; 15:1514. [PMID: 38374047 PMCID: PMC10876973 DOI: 10.1038/s41467-024-45922-8] [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: 09/06/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol's known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.
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Affiliation(s)
- Niklas Brake
- Quantiative Life Sciences PhD Program, McGill University, Montreal, Canada
- Department of Physiology, McGill University, Montreal, Canada
| | - Flavie Duc
- Department of Anesthesia, McGill University, Montreal, Canada
| | - Alexander Rokos
- Department of Anesthesia, McGill University, Montreal, Canada
| | | | - Shiva Shahiri
- School of Nursing, McGill University, Montreal, Canada
| | - Anmar Khadra
- Department of Physiology, McGill University, Montreal, Canada.
| | - Gilles Plourde
- Department of Anesthesia, McGill University, Montreal, Canada.
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10
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Francioni V, Tang VD, Brown NJ, Toloza EH, Harnett M. Vectorized instructive signals in cortical dendrites during a brain-computer interface task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565534. [PMID: 37961227 PMCID: PMC10635122 DOI: 10.1101/2023.11.03.565534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence1,2. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments3-7. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.
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Affiliation(s)
- Valerio Francioni
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Vincent D Tang
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Norma J. Brown
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Enrique H.S. Toloza
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Mark Harnett
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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11
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Venkatesan S, Binko MA, Mielnik CA, Ramsey AJ, Lambe EK. Deficits in integrative NMDA receptors caused by Grin1 disruption can be rescued in adulthood. Neuropsychopharmacology 2023; 48:1742-1751. [PMID: 37349472 PMCID: PMC10579298 DOI: 10.1038/s41386-023-01619-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/10/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
Glutamatergic NMDA receptors (NMDAR) are critical for cognitive function, and their reduced expression leads to intellectual disability. Since subpopulations of NMDARs exist in distinct subcellular environments, their functioning may be unevenly vulnerable to genetic disruption. Here, we investigate synaptic and extrasynaptic NMDARs on the major output neurons of the prefrontal cortex in mice deficient for the obligate NMDAR subunit encoded by Grin1 and wild-type littermates. With whole-cell recording in brain slices, we find that single, low-intensity stimuli elicit surprisingly-similar glutamatergic synaptic currents in both genotypes. By contrast, clear genotype differences emerge with manipulations that recruit extrasynaptic NMDARs, including stronger, repetitive, or pharmacological stimulation. These results reveal a disproportionate functional deficit of extrasynaptic NMDARs compared to their synaptic counterparts. To probe the repercussions of this deficit, we examine an NMDAR-dependent phenomenon considered a building block of cognitive integration, basal dendrite plateau potentials. Since we find this phenomenon is readily evoked in wild-type but not in Grin1-deficient mice, we ask whether plateau potentials can be restored by an adult intervention to increase Grin1 expression. This genetic manipulation, previously shown to restore cognitive performance in adulthood, successfully rescues electrically-evoked basal dendrite plateau potentials after a lifetime of NMDAR compromise. Taken together, our work demonstrates NMDAR subpopulations are not uniformly vulnerable to the genetic disruption of their obligate subunit. Furthermore, the window for functional rescue of the more-sensitive integrative NMDARs remains open into adulthood.
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Affiliation(s)
| | - Mary A Binko
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Catharine A Mielnik
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Amy J Ramsey
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Evelyn K Lambe
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
- Department of OBGYN, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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Chen L, Daniels S, Dvorak R, Chu HY. Reduced thalamic excitation to motor cortical pyramidal tract neurons in parkinsonism. SCIENCE ADVANCES 2023; 9:eadg3038. [PMID: 37611096 PMCID: PMC10446482 DOI: 10.1126/sciadv.adg3038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/21/2023] [Indexed: 08/25/2023]
Abstract
Degeneration of midbrain dopaminergic (DA) neurons alters the connectivity and functionality of the basal ganglia-thalamocortical circuits in Parkinson's disease (PD). Particularly, the aberrant outputs of the primary motor cortex (M1) contribute to parkinsonian motor deficits. However, cortical adaptations at cellular and synaptic levels in parkinsonism remain poorly understood. Using multidisciplinary approaches, we found that DA degeneration induces cell subtype- and input-specific reduction of thalamic excitation to M1 pyramidal tract (PT) neurons. At molecular level, we identified that N-methyl-d-aspartate (NMDA) receptors play a key role in mediating the reduced thalamocortical excitation to PT neurons. At circuit level, we showed that the reduced thalamocortical transmission in parkinsonian mice can be rescued by chemogenetically suppressing basal ganglia outputs. Together, our data suggest that cell subtype- and synapse-specific adaptations in M1 contribute to altered cortical outputs in parkinsonism and are important aspects of PD pathophysiology.
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Affiliation(s)
- Liqiang Chen
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503 USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
| | - Samuel Daniels
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503 USA
| | - Rachel Dvorak
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503 USA
| | - Hong-Yuan Chu
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503 USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
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13
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Tang Y, Zhang X, An L, Yu Z, Liu JK. Diverse role of NMDA receptors for dendritic integration of neural dynamics. PLoS Comput Biol 2023; 19:e1011019. [PMID: 37036844 PMCID: PMC10085026 DOI: 10.1371/journal.pcbi.1011019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.
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Affiliation(s)
- Yuanhong Tang
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Xingyu Zhang
- Guangzhou Institute of Technology, Xidian University, Guangzhou, China
| | - Lingling An
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Jian K Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
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Vardalaki D, Pham TLD, Frosch MP, Cosgrove GR, Richardson M, Cash SS, Harnett MT. Patch2MAP combines patch-clamp electrophysiology with super-resolution structural and protein imaging in identified single neurons without genetic modification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533452. [PMID: 36993722 PMCID: PMC10055279 DOI: 10.1101/2023.03.20.533452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Recent developments in super-resolution microscopy have revolutionized the study of cell biology. However, dense tissues require exogenous protein expression for single cell morphological contrast. In the nervous system, many cell types and species of interest - particularly human - are not amenable to genetic modification and/or exhibit intricate anatomical specializations which make cellular delineation challenging. Here, we present a method for full morphological labeling of individual neurons from any species or cell type for subsequent cell-resolved protein analysis without genetic modification. Our method, which combines patch-clamp electrophysiology with epitope-preserving magnified analysis of proteome (eMAP), further allows for correlation of physiological properties with subcellular protein expression. We applied Patch2MAP to individual spiny synapses in human cortical pyramidal neurons and demonstrated that electrophysiological AMPA-to-NMDA receptor ratios correspond tightly to respective protein expression levels. Patch2MAP thus permits combined subcellular functional, anatomical, and proteomic analyses of any cell, opening new avenues for direct molecular investigation of the human brain in health and disease.
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Quantitative Fluorescence Analysis Reveals Dendrite-Specific Thalamocortical Plasticity in L5 Pyramidal Neurons during Learning. J Neurosci 2023; 43:584-600. [PMID: 36639912 PMCID: PMC9888508 DOI: 10.1523/jneurosci.1372-22.2022] [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: 07/07/2022] [Revised: 10/28/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
High-throughput anatomic data can stimulate and constrain new hypotheses about how neural circuits change in response to experience. Here, we use fluorescence-based reagents for presynaptic and postsynaptic labeling to monitor changes in thalamocortical synapses onto different compartments of layer 5 (L5) pyramidal (Pyr) neurons in somatosensory (barrel) cortex from mixed-sex mice during whisker-dependent learning (Audette et al., 2019). Using axonal fills and molecular-genetic tags for synapse identification in fixed tissue from Rbp4-Cre transgenic mice, we found that thalamocortical synapses from the higher-order posterior medial thalamic nucleus showed rapid morphologic changes in both presynaptic and postsynaptic structures at the earliest stages of sensory association training. Detected increases in thalamocortical synaptic size were compartment specific, occurring selectively in the proximal dendrites onto L5 Pyr and not at inputs onto their apical tufts in L1. Both axonal and dendritic changes were transient, normalizing back to baseline as animals became expert in the task. Anatomical measurements were corroborated by electrophysiological recordings at different stages of training. Thus, fluorescence-based analysis of input- and target-specific synapses can reveal compartment-specific changes in synapse properties during learning.SIGNIFICANCE STATEMENT Synaptic changes underlie the cellular basis of learning, experience, and neurologic diseases. Neuroanatomical methods to assess synaptic plasticity can provide critical spatial information necessary for building models of neuronal computations during learning and experience but are technically and fiscally intensive. Here, we describe a confocal fluorescence microscopy-based analytical method to assess input, cell type, and dendritic location-specific synaptic plasticity in a sensory learning assay. Our method not only confirms prior electrophysiological measurements but allows us to predict functional strength of synapses in a pathway-specific manner. Our findings also indicate that changes in primary sensory cortices are transient, occurring during early learning. Fluorescence-based synapse identification can be an efficient and easily adopted approach to study synaptic changes in a variety of experimental paradigms.
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16
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Fang M, Poskanzer C, Anzellotti S. Multivariate connectivity: A brief introduction and an open question. Front Neurosci 2023; 16:1082120. [PMID: 36704006 PMCID: PMC9871770 DOI: 10.3389/fnins.2022.1082120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/16/2022] [Indexed: 01/11/2023] Open
Affiliation(s)
- Mengting Fang
- University of Pennsylvania, Philadelphia, PA, United States
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17
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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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Poskanzer C, Anzellotti S. Functional coordinates: Modeling interactions between brain regions as points in a function space. Netw Neurosci 2022; 6:1296-1315. [PMID: 38800459 PMCID: PMC11117108 DOI: 10.1162/netn_a_00264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/22/2022] [Indexed: 05/29/2024] Open
Abstract
Here, we propose a novel technique to investigate nonlinear interactions between brain regions that captures both the strength and type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in separate brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite polynomials as bases, we estimate a subset of these values that serve as "functional coordinates," characterizing the interaction between BOLD activity across brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that functional coordinates detect statistical dependence even when correlations ("functional connectivity") approach zero. We then use functional coordinates to examine neural interactions with a chosen seed region: the fusiform face area (FFA). Using k-means clustering across each voxel's functional coordinates, we illustrate that adding nonlinear basis functions allows for the discrimination of interregional interactions that are otherwise grouped together when using only linear dependence. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.
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Affiliation(s)
- Craig Poskanzer
- Department of Psychology, Columbia University, New York City, NY, USA
- Department of Psychology and Neuroscience, Boston College, Boston, MA, USA
| | - Stefano Anzellotti
- Department of Psychology and Neuroscience, Boston College, Boston, MA, USA
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Jamann N, Kole MHP. Connecting axons and dendrites: An oblique view. Neuron 2022; 110:1438-1440. [PMID: 35512635 DOI: 10.1016/j.neuron.2022.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Cortical pyramidal neurons receive thousands of synaptic inputs and transform these into action potential output. In this issue of Neuron, Lafourcade et al. (2022) demonstrate that distinct long-range projections to retrosplenial cortex pyramidal neurons are coupled to diverse modes of dendritic integration.
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Affiliation(s)
- Nora Jamann
- Axonal Signaling Group, Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy for Arts and Sciences (KNAW), Meibergdreef 47, 1105 BA, Amsterdam, the Netherlands; Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands
| | - Maarten H P Kole
- Axonal Signaling Group, Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy for Arts and Sciences (KNAW), Meibergdreef 47, 1105 BA, Amsterdam, the Netherlands; Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands.
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Iyer A, Grewal K, Velu A, Souza LO, Forest J, Ahmad S. Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments. Front Neurorobot 2022; 16:846219. [PMID: 35574225 PMCID: PMC9100780 DOI: 10.3389/fnbot.2022.846219] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows: first, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results in both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.
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Affiliation(s)
- Abhiram Iyer
- Numenta, Redwood City, CA, United States
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Akash Velu
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | | | - Jeremy Forest
- Department of Psychology, Cornell University, Ithaca, NY, United States
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