1
|
Sun SED, Levenstein D, Li B, Mandelberg N, Chenouard N, Suutari BS, Sanchez S, Tian G, Rinzel J, Buzsáki G, Tsien RW. Synaptic homeostasis transiently leverages Hebbian mechanisms for a multiphasic response to inactivity. Cell Rep 2024; 43:113839. [PMID: 38507409 DOI: 10.1016/j.celrep.2024.113839] [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: 09/13/2022] [Revised: 12/19/2023] [Accepted: 02/05/2024] [Indexed: 03/22/2024] Open
Abstract
Homeostatic regulation of synapses is vital for nervous system function and key to understanding a range of neurological conditions. Synaptic homeostasis is proposed to operate over hours to counteract the destabilizing influence of long-term potentiation (LTP) and long-term depression (LTD). The prevailing view holds that synaptic scaling is a slow first-order process that regulates postsynaptic glutamate receptors and fundamentally differs from LTP or LTD. Surprisingly, we find that the dynamics of scaling induced by neuronal inactivity are not exponential or monotonic, and the mechanism requires calcineurin and CaMKII, molecules dominant in LTD and LTP. Our quantitative model of these enzymes reconstructs the unexpected dynamics of homeostatic scaling and reveals how synapses can efficiently safeguard future capacity for synaptic plasticity. This mechanism of synaptic adaptation supports a broader set of homeostatic changes, including action potential autoregulation, and invites further inquiry into how such a mechanism varies in health and disease.
Collapse
Affiliation(s)
- Simón E D Sun
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Daniel Levenstein
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, 3810 University Street, Montreal, QC, Canada
| | - Boxing Li
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Neuroscience Program, Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510810, China
| | - Nataniel Mandelberg
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Nicolas Chenouard
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Sorbonne Université, INSERM U1127, UMR CNRS 7225, Institut du Cerveau (ICM), 47 bld de l'hôpital, 75013 Paris, France
| | - Benjamin S Suutari
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Sandrine Sanchez
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Guoling Tian
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - John Rinzel
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - György Buzsáki
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Richard W Tsien
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA.
| |
Collapse
|
2
|
O'Neill KM, Anderson ED, Mukherjee S, Gandu S, McEwan SA, Omelchenko A, Rodriguez AR, Losert W, Meaney DF, Babadi B, Firestein BL. Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry. Commun Biol 2023; 6:1278. [PMID: 38110605 PMCID: PMC10728104 DOI: 10.1038/s42003-023-05638-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 11/27/2023] [Indexed: 12/20/2023] Open
Abstract
Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider.
Collapse
Affiliation(s)
- Kate M O'Neill
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Biomedical Engineering Graduate Program, Rutgers University, Piscataway, NJ, USA
- Institute for Physical Science & Technology, University of Maryland, College Park, MD, USA
| | - Erin D Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Srinivasa Gandu
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Cell and Developmental Biology Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Sara A McEwan
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Neuroscience Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Anton Omelchenko
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Neuroscience Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Ana R Rodriguez
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Biomedical Engineering Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Wolfgang Losert
- Department of Physics, University of Maryland, College Park, MD, USA
- Institute for Physical Science & Technology, University of Maryland, College Park, MD, USA
| | - David F Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Bonnie L Firestein
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA.
| |
Collapse
|
3
|
Elliott MAT, Schweiger HE, Robbins A, Vera-Choqqueccota S, Ehrlich D, Hernandez S, Voitiuk K, Geng J, Sevetson JL, Core C, Rosen YM, Teodorescu M, Wagner NO, Haussler D, Mostajo-Radji MA. Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education. eNeuro 2023; 10:ENEURO.0308-23.2023. [PMID: 38016807 PMCID: PMC10755643 DOI: 10.1523/eneuro.0308-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/16/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.
Collapse
Affiliation(s)
- Matthew A T Elliott
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Hunter E Schweiger
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Ash Robbins
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Samira Vera-Choqqueccota
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Drew Ehrlich
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Computational Media, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Sebastian Hernandez
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Kateryna Voitiuk
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Jinghui Geng
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Jess L Sevetson
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Cordero Core
- Scientific Software Engineering Center, eScience Institute, University of Washington, Seattle, WA 98195
| | - Yohei M Rosen
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Mircea Teodorescu
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Nico O Wagner
- College of Arts and Sciences, University of San Francisco, San Francisco, CA 94117
| | - David Haussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060
| | - Mohammed A Mostajo-Radji
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA 95060
| |
Collapse
|
4
|
Elliott MA, Schweiger HE, Robbins A, Vera-Choqqueccota S, Ehrlich D, Hernandez S, Voitiuk K, Geng J, Sevetson JL, Rosen YM, Teodorescu M, Wagner NO, Haussler D, Mostajo-Radji MA. Internet-connected cortical organoids for project-based stem cell and neuroscience education. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.546418. [PMID: 37503236 PMCID: PMC10369936 DOI: 10.1101/2023.07.13.546418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course, and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.
Collapse
Affiliation(s)
- Matthew A.T. Elliott
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Hunter E. Schweiger
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Ash Robbins
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Samira Vera-Choqqueccota
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Drew Ehrlich
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Computational Media, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Sebastian Hernandez
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Kateryna Voitiuk
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Jinghui Geng
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Jess L. Sevetson
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Yohei M. Rosen
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Mircea Teodorescu
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Nico O. Wagner
- College of Arts and Sciences, University of San Francisco, San Francisco, CA, 94117, USA
| | - David Haussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Mohammed A. Mostajo-Radji
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| |
Collapse
|
5
|
Chauhan T, Masquelier T, Cottereau BR. Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity. Front Neurosci 2021; 15:727448. [PMID: 34602970 PMCID: PMC8480265 DOI: 10.3389/fnins.2021.727448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
The early visual cortex is the site of crucial pre-processing for more complex, biologically relevant computations that drive perception and, ultimately, behaviour. This pre-processing is often studied under the assumption that neural populations are optimised for the most efficient (in terms of energy, information, spikes, etc.) representation of natural statistics. Normative models such as Independent Component Analysis (ICA) and Sparse Coding (SC) consider the phenomenon as a generative, minimisation problem which they assume the early cortical populations have evolved to solve. However, measurements in monkey and cat suggest that receptive fields (RFs) in the primary visual cortex are often noisy, blobby, and symmetrical, making them sub-optimal for operations such as edge-detection. We propose that this suboptimality occurs because the RFs do not emerge through a global minimisation of generative error, but through locally operating biological mechanisms such as spike-timing dependent plasticity (STDP). Using a network endowed with an abstract, rank-based STDP rule, we show that the shape and orientation tuning of the converged units are remarkably close to single-cell measurements in the macaque primary visual cortex. We quantify this similarity using physiological parameters (frequency-normalised spread vectors), information theoretic measures [Kullback–Leibler (KL) divergence and Gini index], as well as simulations of a typical electrophysiology experiment designed to estimate orientation tuning curves. Taken together, our results suggest that compared to purely generative schemes, process-based biophysical models may offer a better description of the suboptimality observed in the early visual cortex.
Collapse
Affiliation(s)
- Tushar Chauhan
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France.,Centre National de la Recherche Scientifique, Toulouse, France
| | - Timothée Masquelier
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France.,Centre National de la Recherche Scientifique, Toulouse, France
| | - Benoit R Cottereau
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France.,Centre National de la Recherche Scientifique, Toulouse, France
| |
Collapse
|
6
|
Peterson EJ, Voytek B. Homeostatic mechanisms may shape the type and duration of oscillatory modulation. J Neurophysiol 2020; 124:168-177. [PMID: 32490710 DOI: 10.1152/jn.00119.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca2+-dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-don't provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response and so can preserve the pyramidal cell's natural dynamic range. Based on these results we speculate that homeostasis may explain why AMPAergic oscillations in cortex, and in hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant and so retain their normal excitatory effect.NEW & NOTEWORTHY The intricate interplay of neuromodulators, like acetylcholine, with homeostasis is well known. The interplay between oscillatory modulation and homeostasis is not. We studied oscillatory modulation and homeostasis for the first time using a simplified model of hippocampus. We report a paradoxical result: Ca-mediated homeostasis causes AMPAergic oscillations to become effectively inhibitory. This result, along with other new observations, means homeostasis might be just as complex and important for oscillations as it is for other neuromodulators.
Collapse
Affiliation(s)
- Erik J Peterson
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.,Department of Cognitive Science, University of California, San Diego, California
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, California.,Neurosciences Graduate Program, University of California, San Diego, California.,Halıcıoğlu Data Science Institute, University of California, San Diego, California
| |
Collapse
|
7
|
Tozzi A, Peters JF. Removing uncertainty in neural networks. Cogn Neurodyn 2020; 14:339-345. [PMID: 32399075 DOI: 10.1007/s11571-020-09574-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/24/2020] [Accepted: 02/19/2020] [Indexed: 01/26/2023] Open
Abstract
Neuroscientists draw lines of separation among structures and functions that they judge different, arbitrarily excluding or including issues in our description, to achieve positive demarcations that permits a pragmatic treatment of the nervous activity based on regularity and uniformity. However, uncertainty due to disconnectedness, lack of information and absence of objects' sharp boundaries is a troubling issue that prevents these scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial neural networks. Starting from the detection of metamorphoses of shapes inside a Euclidean manifold, we propose a technique to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, that allows the detection of topological holes development and disappearance, makes it possible to solve the problem of uncertainty in the assessment of countless dynamical phenomena, such as cognitive processes, protein homeostasis deterioration, fire propagation, wireless sensor networks, migration flows, and cosmic bodies analysis.
Collapse
Affiliation(s)
- Arturo Tozzi
- 1Center for Nonlinear Science, Department of Physics, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - James F Peters
- 2Department of Electrical and Computer Engineering, University of Manitoba, Winnpeg, MB R3T 5V6 Canada.,3Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey
| |
Collapse
|
8
|
Ocker GK, Doiron B. Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity. Cereb Cortex 2019; 29:937-951. [PMID: 29415191 PMCID: PMC7963120 DOI: 10.1093/cercor/bhy001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 01/01/2018] [Accepted: 01/05/2018] [Indexed: 12/15/2022] Open
Abstract
The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.
Collapse
Affiliation(s)
- Gabriel Koch Ocker
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brent Doiron
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
9
|
Nicola W, Hellyer PJ, Campbell SA, Clopath C. Chaos in homeostatically regulated neural systems. CHAOS (WOODBURY, N.Y.) 2018; 28:083104. [PMID: 30180641 PMCID: PMC6684369 DOI: 10.1063/1.5026489] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 07/09/2018] [Indexed: 06/08/2023]
Abstract
Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimensional dynamics remains elusive. Here, we consider Wilson-Cowan networks and demonstrate through numerical and analytical work that homeostatic regulation of the network firing rates can paradoxically lead to a rich dynamical repertoire. The dynamics include mixed-mode oscillations, mixed-mode chaos, and chaotic synchronization when the homeostatic plasticity operates on a moderately slower time scale than the firing rates. This is true for a single recurrently coupled node, pairs of reciprocally coupled nodes without self-coupling, and networks coupled through experimentally determined weights derived from functional magnetic resonance imaging data. In all cases, the stability of the homeostatic set point is analytically determined or approximated. The dynamics at the network level are directly determined by the behavior of a single node system through synchronization in both oscillatory and non-oscillatory states. Our results demonstrate that rich dynamics can be preserved under homeostatic regulation or even be caused by homeostatic regulation.
Collapse
Affiliation(s)
- Wilten Nicola
- Department of Bioengineering, Imperial College London
| | | | | | | |
Collapse
|
10
|
Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization. Proc Natl Acad Sci U S A 2018; 115:E1356-E1365. [PMID: 29378970 DOI: 10.1073/pnas.1712989115] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding the origin, nature, and functional significance of complex patterns of neural activity, as recorded by diverse electrophysiological and neuroimaging techniques, is a central challenge in neuroscience. Such patterns include collective oscillations emerging out of neural synchronization as well as highly heterogeneous outbursts of activity interspersed by periods of quiescence, called "neuronal avalanches." Much debate has been generated about the possible scale invariance or criticality of such avalanches and its relevance for brain function. Aimed at shedding light onto this, here we analyze the large-scale collective properties of the cortex by using a mesoscopic approach following the principle of parsimony of Landau-Ginzburg. Our model is similar to that of Wilson-Cowan for neural dynamics but crucially, includes stochasticity and space; synaptic plasticity and inhibition are considered as possible regulatory mechanisms. Detailed analyses uncover a phase diagram including down-state, synchronous, asynchronous, and up-state phases and reveal that empirical findings for neuronal avalanches are consistently reproduced by tuning our model to the edge of synchronization. This reveals that the putative criticality of cortical dynamics does not correspond to a quiescent-to-active phase transition as usually assumed in theoretical approaches but to a synchronization phase transition, at which incipient oscillations and scale-free avalanches coexist. Furthermore, our model also accounts for up and down states as they occur (e.g., during deep sleep). This approach constitutes a framework to rationalize the possible collective phases and phase transitions of cortical networks in simple terms, thus helping to shed light on basic aspects of brain functioning from a very broad perspective.
Collapse
|
11
|
Turrigiano GG. The dialectic of Hebb and homeostasis. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2016.0258. [PMID: 28093556 DOI: 10.1098/rstb.2016.0258] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2016] [Indexed: 01/12/2023] Open
Abstract
It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.
Collapse
|
12
|
Zenke F, Gerstner W. Hebbian plasticity requires compensatory processes on multiple timescales. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2016.0259. [PMID: 28093557 PMCID: PMC5247595 DOI: 10.1098/rstb.2016.0259] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2016] [Indexed: 01/19/2023] Open
Abstract
We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.
Collapse
Affiliation(s)
- Friedemann Zenke
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Wulfram Gerstner
- Brain Mind Institute, School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne EPFL, Switzerland
| |
Collapse
|
13
|
Richter LMA, Gjorgjieva J. Understanding neural circuit development through theory and models. Curr Opin Neurobiol 2017; 46:39-47. [DOI: 10.1016/j.conb.2017.07.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 07/07/2017] [Accepted: 07/10/2017] [Indexed: 11/25/2022]
|
14
|
Keck T, Toyoizumi T, Chen L, Doiron B, Feldman DE, Fox K, Gerstner W, Haydon PG, Hübener M, Lee HK, Lisman JE, Rose T, Sengpiel F, Stellwagen D, Stryker MP, Turrigiano GG, van Rossum MC. Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160158. [PMID: 28093552 PMCID: PMC5247590 DOI: 10.1098/rstb.2016.0158] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2016] [Indexed: 11/12/2022] Open
Abstract
We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the meeting and finally suggest potential directions of research that we believe are most promising to develop an understanding of how these two forms of plasticity interact to facilitate functional changes in the brain.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.
Collapse
Affiliation(s)
- Tara Keck
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - Lu Chen
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel E Feldman
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Kevin Fox
- Division of Neuroscience, University of Cardiff, Cardiff, Wales, UK
| | - Wulfram Gerstner
- Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Mark Hübener
- Department of Cellular and Systems Neuroscience, Max Planck Institute of Neurobiology, Martinsried, Bayern, Germany
| | - Hey-Kyoung Lee
- The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
| | - John E Lisman
- Department of Biology, Brandeis University, Waltham, MA, USA
| | - Tobias Rose
- Department of Cellular and Systems Neuroscience, Max Planck Institute of Neurobiology, Martinsried, Bayern, Germany
| | - Frank Sengpiel
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- Division of Neuroscience, University of Cardiff, Cardiff, Wales, UK
| | - David Stellwagen
- Centre for Research in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Michael P Stryker
- Sandler Neurosciences Center, University of California, San Francisco, CA, USA
| | | | | |
Collapse
|
15
|
|