Synaptic Plasticity: Taming the Beast
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DIVERGENT SCALING of MINIATURE EXCITATORY POST-SYNAPTIC CURRENT AMPLITUDES in HOMEOSTATIC PLASTICITY a Dissertation Submitted In
DIVERGENT SCALING OF MINIATURE EXCITATORY POST-SYNAPTIC CURRENT AMPLITUDES IN HOMEOSTATIC PLASTICITY A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by AMANDA L. HANES M.S., Wright State University, 2009 B.S.C.S., Wright State University, 2006 2018 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 12, 2018 I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Amanda L. Hanes ENTITLED Divergent scaling of miniature post-synaptic current amplitudes in homeostatic plasticity BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. ________________________________ Kathrin L. Engisch, Ph.D Dissertation Director ________________________________ Mill W. Miller, Ph.D. Director, Biomedical Sciences PhD Program ________________________________ Barry Milligan, Ph.D. Interim Dean of the Graduate School Committee on Final Examination: ________________________________ Mark Rich, M.D./Ph.D. ________________________________ David Ladle, Ph.D. ________________________________ Michael Raymer, Ph.D. ________________________________ Courtney Sulentic, Ph.D. ABSTRACT Hanes, Amanda L. Ph.D. Biomedical Sciences PhD Program, Wright State University, 2018. Divergent scaling of miniature excitatory post-synaptic current amplitudes in homeostatic plasticity Synaptic plasticity, the ability of neurons to modulate their inputs in response to changing stimuli, occurs in two forms which have opposing effects on synaptic physiology. Hebbian plasticity induces rapid, persistent changes at individual synapses in a positive feedback manner. Homeostatic plasticity is a negative feedback effect that responds to chronic changes in network activity by inducing opposing, network-wide changes in synaptic strength and restoring activity to its original level. The changes in synaptic strength can be measured as changes in the amplitudes of miniature post- synaptic excitatory currents (mEPSCs). -
Rab3a As a Modulator of Homeostatic Synaptic Plasticity
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2014 Rab3A as a Modulator of Homeostatic Synaptic Plasticity Andrew G. Koesters Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Biomedical Engineering and Bioengineering Commons Repository Citation Koesters, Andrew G., "Rab3A as a Modulator of Homeostatic Synaptic Plasticity" (2014). Browse all Theses and Dissertations. 1246. https://corescholar.libraries.wright.edu/etd_all/1246 This Dissertation is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. RAB3A AS A MODULATOR OF HOMEOSTATIC SYNAPTIC PLASTICITY A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy By ANDREW G. KOESTERS B.A., Miami University, 2004 2014 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL August 22, 2014 I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Andrew G. Koesters ENTITLED Rab3A as a Modulator of Homeostatic Synaptic Plasticity BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. Kathrin Engisch, Ph.D. Dissertation Director Mill W. Miller, Ph.D. Director, Biomedical Sciences Ph.D. Program Committee on Final Examination Robert E. W. Fyffe, Ph.D. Vice President for Research Dean of the Graduate School Mark Rich, M.D./Ph.D. David Ladle, Ph.D. F. Javier Alvarez-Leefmans, M.D./Ph.D. Lynn Hartzler, Ph.D. -
The Synaptic Weight Matrix
The Synaptic Weight Matrix: Dynamics, Symmetry Breaking, and Disorder Francesco Fumarola Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2018 © 2018 Francesco Fumarola All rights reserved ABSTRACT The Synaptic Weight Matrix: Dynamics, Symmetry Breaking, and Disorder Francesco Fumarola A key role in simplified models of neural circuitry (Wilson and Cowan, 1972) is played by the matrix of synaptic weights, also called connectivity matrix, whose elements describe the amount of influence the firing of one neuron has on another. Biologically, this matrix evolves over time whether or not sensory inputs are present, and symmetries possessed by the internal dynamics of the network may break up sponta- neously, as found in the development of the visual cortex (Hubel and Wiesel, 1977). In this thesis, a full analytical treatment is provided for the simplest case of such a biologi- cal phenomenon, a single dendritic arbor driven by correlation-based dynamics (Linsker, 1988). Borrowing methods from the theory of Schrödinger operators, a complete study of the model is performed, analytically describing the break-up of rotational symmetry that leads to the functional specialization of cells. The structure of the eigenfunctions is calcu- lated, lower bounds are derived on the critical value of the correlation length, and explicit expressions are obtained for the long-term profile of receptive fields, i.e. the dependence of neural activity on external inputs. The emergence of a functional architecture of orientation preferences in the cortex is another crucial feature of visual information processing. -
Disruption of NMDA Receptor Function Prevents Normal Experience
Research Articles: Development/Plasticity/Repair Disruption of NMDA receptor function prevents normal experience-dependent homeostatic synaptic plasticity in mouse primary visual cortex https://doi.org/10.1523/JNEUROSCI.2117-18.2019 Cite as: J. Neurosci 2019; 10.1523/JNEUROSCI.2117-18.2019 Received: 16 August 2018 Revised: 7 August 2019 Accepted: 8 August 2019 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.jneurosci.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2019 the authors Rodriguez et al. 1 Disruption of NMDA receptor function prevents normal experience-dependent homeostatic 2 synaptic plasticity in mouse primary visual cortex 3 4 Gabriela Rodriguez1,4, Lukas Mesik2,3, Ming Gao2,5, Samuel Parkins1, Rinki Saha2,6, 5 and Hey-Kyoung Lee1,2,3 6 7 8 1. Cell Molecular Developmental Biology and Biophysics (CMDB) Graduate Program, 9 Johns Hopkins University, Baltimore, MD 21218 10 2. Department of Neuroscience, Mind/Brain Institute, Johns Hopkins University, Baltimore, 11 MD 21218 12 3. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 13 21218 14 4. Current address: Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458 15 5. Current address: Division of Neurology, Barrow Neurological Institute, Pheonix, AZ 16 85013 17 6. Current address: Department of Psychiatry, Columbia University, New York, NY10032 18 19 Abbreviated title: NMDARs in homeostatic synaptic plasticity of V1 20 21 Corresponding Author: Hey-Kyoung Lee, Ph.D. -
Synaptic Plasticity in Correlated Balanced Networks
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.26.061515; this version posted April 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Synaptic Plasticity in Correlated Balanced Networks Alan Eric Akil Department of Mathematics, University of Houston, Houston, Texas, United States of America Robert Rosenbaum Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, United States of America∗ Kreˇsimir Josi´cy Department of Mathematics, University of Houston, Houston, Texas, United States of America z (Dated: April 2020) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.26.061515; this version posted April 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory{ inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How does the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a general theory of plasticity in balanced networks. -
Energy Efficient Synaptic Plasticity Ho Ling Li1, Mark CW Van Rossum1,2*
RESEARCH ARTICLE Energy efficient synaptic plasticity Ho Ling Li1, Mark CW van Rossum1,2* 1School of Psychology, University of Nottingham, Nottingham, United Kingdom; 2School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom Abstract Many aspects of the brain’s design can be understood as the result of evolutionary drive toward metabolic efficiency. In addition to the energetic costs of neural computation and transmission, experimental evidence indicates that synaptic plasticity is metabolically demanding as well. As synaptic plasticity is crucial for learning, we examine how these metabolic costs enter in learning. We find that when synaptic plasticity rules are naively implemented, training neural networks requires extremely large amounts of energy when storing many patterns. We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms. This algorithm, termed synaptic caching, boosts energy efficiency manifold and can be used with any plasticity rule, including back-propagation. Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally, including synaptic tagging and capture phenomena. Furthermore, our results are relevant for energy efficient neuromorphic designs. Introduction The human brain only weighs 2% of the total body mass but is responsible for 20% of resting metab- olism (Attwell and Laughlin, 2001; Harris et al., 2012). The brain’s energy need is believed to have shaped many aspects of its design, such as its sparse coding strategy (Levy and Baxter, 1996; Len- nie, 2003), the biophysics of the mammalian action potential (Alle et al., 2009; Fohlmeister, 2009), *For correspondence: and synaptic failure (Levy and Baxter, 2002; Harris et al., 2012). -
Control of Homeostatic Synaptic Plasticity by AKAP-Anchored Kinase and Phosphatase Regulation of Ca2+-Permeable AMPA Receptors
This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. Research Articles: Cellular/Molecular Control of Homeostatic Synaptic Plasticity by AKAP-Anchored Kinase and Phosphatase Regulation of Ca2+-Permeable AMPA Receptors Jennifer L. Sanderson1, John D. Scott3,4 and Mark L. Dell'Acqua1,2 1Department of Pharmacology 2Program in Neuroscience, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA. 3Howard Hughes Medical Institute 4Department of Pharmacology, University of Washington School of Medicine, Seattle, WA 98195, USA. DOI: 10.1523/JNEUROSCI.2362-17.2018 Received: 21 August 2017 Revised: 17 January 2018 Accepted: 6 February 2018 Published: 13 February 2018 Author contributions: J.S., J.D.S., and M.L.D. designed research; J.S. performed research; J.S. and M.L.D. analyzed data; J.S., J.D.S., and M.L.D. wrote the paper; J.D.S. contributed unpublished reagents/analytic tools. Conflict of Interest: The authors declare no competing financial interests. This work was supported by NIH grant R01NS040701 (to M.L.D.). Contents are the authors' sole responsibility and do not necessarily represent official NIH views. The authors declare no competing financial interests. We thank Dr. Jason Aoto for critical reading of the manuscript. Corresponding author: Mark L. Dell'Acqua, Department of Pharmacology, University of Colorado School of Medicine, Mail Stop 8303, RC1-North, 12800 East 19th Ave Aurora, CO 80045. Email: [email protected] Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2362-17.2018 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. -
Arxiv:2103.12649V1 [Q-Bio.NC] 23 Mar 2021
A synapse-centric account of the free energy principle David Kappel and Christian Tetzlaff March 24, 2021 Abstract The free energy principle (FEP) is a mathematical framework that describes how biological systems self- organize and survive in their environment. This principle provides insights on multiple scales, from high-level behavioral and cognitive functions such as attention or foraging, down to the dynamics of specialized cortical microcircuits, suggesting that the FEP manifests on several levels of brain function. Here, we apply the FEP to one of the smallest functional units of the brain: single excitatory synaptic connections. By focusing on an experimentally well understood biological system we are able to derive learning rules from first principles while keeping assumptions minimal. This synapse-centric account of the FEP predicts that synapses interact with the soma of the post-synaptic neuron through stochastic synaptic releases to probe their behavior and use back-propagating action potentials as feedback to update the synaptic weights. The emergent learning rules are regulated triplet STDP rules that depend only on the timing of the pre- and post-synaptic spikes and the internal states of the synapse. The parameters of the learning rules are fully determined by the parameters of the post-synaptic neuron model, suggesting a close interplay between the synaptic and somatic compartment and making precise predictions about the synaptic dynamics. The synapse-level uncertainties automatically lead to representations of uncertainty on the network level that manifest in ambiguous situations. We show that the FEP learning rules can be applied to spiking neural networks for supervised and unsupervised learning and for a closed loop learning task where a behaving agent interacts with an environment. -
Improved Expressivity Through Dendritic Neural Networks
Improved Expressivity Through Dendritic Neural Networks Xundong Wu Xiangwen Liu Wei Li Qing Wu School of Computer Science and Technology Hangzhou Dianzi University, Hangzhou, China [email protected], [email protected] A typical biological neuron, such as a pyramidal neuron of the neocortex, receives thousands of afferent synaptic inputs on its dendrite tree and sends the efferent axonal output downstream. In typical artificial neural networks, dendrite trees are modeled as linear structures that funnel weighted synaptic inputs to the cell bodies. However, numerous experimental and theoretical studies have shown that dendritic arbors are far more than simple linear accumulators. That is, synaptic inputs can actively modulate their neighboring synaptic activities; therefore, the dendritic structures are highly nonlinear. In this study, we model such local nonlinearity of dendritic trees with our dendritic neural network (DENN) structure and apply this structure to typical machine learning tasks. Equipped with localized nonlinearities, DENNs can attain greater model expressivity than regular neural networks while maintaining efficient network inference. Such strength is evidenced by the increased fitting power when we train DENNs with supervised machine learning tasks. We also empirically show that the locality structure of DENNs can improve the generalization performance, as exemplified by DENNs outranking naive deep neural network architectures when tested on classification tasks from the UCI machine learning repository. 1 Introduction Deep learning algorithms have made remarkable achievements in a vast array of fields over the past few years. Notably, deep convolutional neural networks have revolutionized computer vision research and real-world applications. Inspired by biological neuronal networks in our brains, originally in the form of a perceptron [37], an artificial neural network unit is typically constructed as a simple weighted sum of synaptic inputs followed by feeding the summation result through an activation function. -
The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized
bioRxiv preprint doi: https://doi.org/10.1101/260950; this version posted August 27, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Manuscript SUBMITTED TO eLife 1 The INTERPLAY OF SYNAPTIC PLASTICITY 2 AND SCALING ENABLES self-orGANIZED 3 FORMATION AND ALLOCATION OF MULTIPLE 4 MEMORY REPRESENTATIONS 1,2†§* 1,2† 1,2 5 Johannes Maria Auth , Timo Nachstedt , Christian TETZLAff *For CORRespondence: ThirD INSTITUTE OF Physics, Georg-August-Universität Göttingen, Germany; Bernstein [email protected] 6 1 2 (JMA) Center FOR Computational Neuroscience, Georg-August-Universität Göttingen, Germany 7 †These AUTHORS CONTRIBUTED EQUALLY TO THIS WORK 8 PrESENT ADDRess: §Department OF 9 NeurOBIOLOGY, The WEIZMANN AbstrACT IT IS COMMONLY ASSUMED THAT MEMORIES ABOUT EXPERIENCED STIMULI ARE REPRESENTED BY INSTITUTE OF Science, Rehovot, ISRAEL10 GROUPS OF HIGHLY INTERCONNECTED NEURONS CALLED CELL assemblies. This REQUIRES ALLOCATING AND STORING 11 INFORMATION IN THE NEURAL CIRCUITRY, WHICH HAPPENS THROUGH SYNAPTIC WEIGHT adaptation. IT Remains, 12 HOWEver, LARGELY UNKNOWN HOW MEMORY ALLOCATION AND STORAGE CAN BE ACHIEVED AND COORDINATED TO 13 ALLOW FOR A FAITHFUL REPRESENTATION OF MULTIPLE MEMORIES WITHOUT DISRUPTIVE INTERFERENCE BETWEEN 14 them. IN THIS THEORETICAL STUDY, WE SHOW THAT THE INTERPLAY BETWEEN CONVENTIONAL SYNAPTIC PLASTICITY 15 AND HOMEOSTATIC SYNAPTIC SCALING ORGANIZES SYNAPTIC WEIGHT ADAPTATIONS SUCH THAT A NEW STIMULUS 16 FORMS A NEW MEMORY AND WHERE DIffERENT STIMULI ARE ASSIGNED TO DISTINCT CELL assemblies. The 17 RESULTING DYNAMICS CAN REPRODUCE EXPERIMENTAL in-vivo data, FOCUSING ON HOW DIVERSE FACTORS AS 18 NEURONAL EXCITABILITY AND NETWORK CONNECTIVITY, INflUENCE MEMORY formation. -
Synaptic Plasticity As Bayesian Inference
ARTICLES https://doi.org/10.1038/s41593-021-00809-5 Synaptic plasticity as Bayesian inference Laurence Aitchison 1,2 ✉ , Jannes Jegminat 3,4, Jorge Aurelio Menendez 1,5, Jean-Pascal Pfister3,4, Alexandre Pouget 1,6,7 and Peter E. Latham 1,7 Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distribu- tions over weights is the optimal strategy. Here we hypothesize that synapses take that strategy; in essence, when they esti- mate weights, they include error bars. They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability in postsynaptic potential size, with more uncertainty leading to more variability. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They generalize known learning rules, offer an explanation for the large variability in the size of postsynaptic potentials and make falsifiable experimental predictions. o survive, animals must accurately estimate the state of the state-of-the-art adaptive optimization algorithms for artificial neu- world. This estimation problem is plagued by uncertainty: ral networks4. Tnot only is information often extremely limited (for example, Bayesian plasticity is a hypothesis about what synapses com- because it is dark) or ambiguous (for example, a rustle in the bushes pute. It does not, however, tell synapses how to set their weights; could be the wind, or it could be a predator), but sensory recep- for that, a second hypothesis is needed. -
Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System Dimitra-Despoina Pagania, Adam Adamopoulos, Spiridon Likothanassis
Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System Dimitra-Despoina Pagania, Adam Adamopoulos, Spiridon Likothanassis To cite this version: Dimitra-Despoina Pagania, Adam Adamopoulos, Spiridon Likothanassis. Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System. 12th Engineering Appli- cations of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.433-442, 10.1007/978-3-642-23957-1_48. hal-01571328 HAL Id: hal-01571328 https://hal.inria.fr/hal-01571328 Submitted on 2 Aug 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System Dimitra – Despoina Pagania1, Adam Adamopoulos1,2 and Spiridon D. Likothanassis1 1 Pattern Recognition Laboratory, Dept. of Computer Engineering & Informatics, University of Patras, 26500, Patras, Greece 2Dept. of Medicine, Democritus University of Thrace, Alexandroupolis, Greece {pagania, likothan}@ceid.upatras.gr, [email protected] Abstract. We present mathematical models that describe individual neural networks of the Central Nervous System. Three cases are examined, varying in each case the values of the refractory period and the synaptic delay of a neuron.