An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity
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Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits PHILIP J. TULLY Doctoral Thesis Stockholm, Sweden 2017 TRITA-CSC-A-2017:11 ISSN 1653-5723 KTH School of Computer Science and Communication ISRN-KTH/CSC/A-17/11-SE SE-100 44 Stockholm ISBN 978-91-7729-351-4 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläg- ges till offentlig granskning för avläggande av teknologie doktorsexamen i datalogi tisdagen den 9 maj 2017 klockan 13.00 i F3, Lindstedtsvägen 26, Kungl Tekniska högskolan, Valhallavägen 79, Stockholm. © Philip J. Tully, May 2017 Tryck: Universitetsservice US AB iii Abstract Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should osten- sibly propel them towards runaway excitation or quiescence. What dynamical phe- nomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large- scale neuronal simulations. Inspiring some of the most seminal neuroscience exper- iments, theoretical models provide insights that demystify experimental measure- ments and even inform new experiments. In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. -
Neural Interfacing Architecture Enables Enhanced Motor Control and Residual Limb Functionality Postamputation
Neural interfacing architecture enables enhanced motor control and residual limb functionality postamputation Shriya S. Srinivasana,b,c,1, Samantha Gutierrez-Arangoa, Ashley Chia-En Tengd, Erica Israela, Hyungeun Songa,b, Zachary Keith Baileya,e, Matthew J. Cartya,f,c, Lisa E. Freeda,b, and Hugh M. Herra,c,1 aMIT Center for Extreme Bionics, Biomechatronics Group, Massachusetts Institute of Technology, Cambridge, MA 02139; bHarvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139; cHarvard Medical School, Boston, MA 02114; dDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139; eDepartment of Mechanical Engineering, United States Air Force Academy, Colorado Springs, CO 80920; and fDivision of Plastics and Reconstructive Surgery, Brigham and Women’s Hospital, Boston, MA 02114 Edited by Peter L. Strick, University of Pittsburgh, Pittsburgh, PA, and approved December 30, 2020 (received for review September 16, 2020) Despite advancements in prosthetic technologies, patients with excurse, causing cocontraction of muscles and changing gait amputation today suffer great diminution in mobility and quality patterns (11–14). Together, these peripheral and central modi- of life. We have developed a modified below-knee amputation fications result in the poor production of efferent signals for (BKA) procedure that incorporates agonist–antagonist myoneural direct myoelectric control (15, 16). To compensate for these interfaces (AMIs), which surgically preserve and couple agonist– shortcomings, considerable effort has been spent on developing antagonist muscle pairs for the subtalar and ankle joints. AMIs are and deploying pattern-recognition–based myoelectric control designed to restore physiological neuromuscular dynamics, enable strategies (17–19). However, even with these advanced myo- bidirectional neural signaling, and offer greater neuroprosthetic electric devices and controllers, end users find their operation controllability compared to traditional amputation techniques. -
Sensory History Matters for Visual Representation: Implications for Autism
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2015 Sensory History Matters for Visual Representation: Implications for Autism David Alexander Kahn University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Cognitive Psychology Commons, and the Neuroscience and Neurobiology Commons Recommended Citation Kahn, David Alexander, "Sensory History Matters for Visual Representation: Implications for Autism" (2015). Publicly Accessible Penn Dissertations. 1071. https://repository.upenn.edu/edissertations/1071 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1071 For more information, please contact [email protected]. Sensory History Matters for Visual Representation: Implications for Autism Abstract How does the brain represent the enormous variety of the visual world? An approach to this question recognizes the types of information that visual representations maintain. The work in this thesis begins by investigating the neural correlates of perceptual similarity & distinctiveness, using EEG measurements of the evoked response to faces. In considering our results, we recognized that the effects being measured shared intrinsic relationships, both in measurement and in their theoretic basis. Using carry- over fMRI designs, we explored this relationship, ultimately demonstrating a new perspective on stimulus relationships based around sensory history that best explains the modulation of brain responses being measured. The result of this collection of experiments is a unified model of neural response modulation based around the integration of recent sensory history into a continually-updated reference; a "drifting- norm." With this novel framework for understanding neural dynamics, we tested whether cognitive theories of autism spectrum disorder (ASD) might have a foundation in altered neural coding for perceptual information. -
The Neural Dynamics of Perceptual Adaptation to Degraded Speech
Julia Erb: The neural dynamics of perceptual adaptation to degraded speech. Leipzig: Max Planck Institute for Human Cognitive and Brain Sciences, 2014 (MPI Series in Human Cognitive and Brain Sciences; 159) The neural dynamics of perceptual adaptation to degraded speech Impressum Max Planck Institute for Human Cognitive and Brain Sciences, 2014 Diese Arbeit ist unter folgender Creative Commons-Lizenz lizenziert: http://creativecommons.org/licenses/by-nc/3.0 Druck: Sächsisches Druck- und Verlagshaus Direct World, Dresden Titelbild: © Moritz Ellerich / RaumZeitPiraten / Fabelphonetikum (rhizome schematic) 2014 ISBN 978-3-941504-43-1 The neural dynamics of perceptual adaptation to degraded speech Der Fakultät für Biowissenschaften, Pharmazie und Psychologie der Universität Leipzig eingereichte D I S S E R T A T I O N zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) vorgelegt von Julia Erb, M.Sc. (Neuroscience) geboren am 17. September 1985 in Heilbronn Leipzig, den 14. Februar 2014 Acknowledgements I would like to thank several people who supported me and decisively contributed to my work during the last three years at the Max Planck Institute. First, I cordially thank my supervisor Jonas Obleser. Jonas was incredibly supportive in all stages of this thesis, during the conception, analysis and the publication of the experiments. Thank you for your generous encouragement to my development as a scientist. I am obliged to Erich Schröger and Mark Eckert who agreed to assess my work. I thank my colleagues and friends from the Auditory Cognition group, Björn Herrmann, Sung-Joo Lim, Mathias Scharinger, Antje Strauß, Anna Wilsch and Malte Wöstmann, for fruitful discussions in Tuesday morning group meetings and Friday evening Cantona sessions – I miss them. -
Modeling Prediction and Pattern Recognition in the Early Visual and Olfactory Systems
Modeling prediction and pattern recognition in the early visual and olfactory systems BERNHARD A. KAPLAN Doctoral Thesis Stockholm, Sweden 2015 TRITA-CSC-A-2015:10 ISSN-1653-5723 KTH School of Computer Science and Communication ISRN KTH/CSC/A-15/10-SE SE-100 44 Stockholm ISBN 978-91-7595-532-2 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan fram- lägges till offentlig granskning för avläggande av Doctoral Thesis in Computer Science onsdag 27:e maj 2015 klockan 10.00 i F3, Lindstedtsvägen 26, Kungl Tekniska högskolan, Stockholm. © Bernhard A. Kaplan, April 2015 Tryck: Universitetsservice US AB iii Abstract Our senses are our mind’s window to the outside world and deter- mine how we perceive our environment. Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings. However, questions on various lev- els and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level. Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages. In this thesis, numerical simulations of spiking neural networks are used to deal with two essential functions that sensory systems have to solve: pattern recognition and prediction. The focus of this thesis lies on the question as to how neural network connectivity can be used in order to achieve these crucial functions. The guiding ideas of the models presented here are grounded in the probabilistic interpretation of neural signals, Hebbian learning principles and connectionist ideas. -
Neural Mechanisms of Actions of Transcranial Electrical Stimulation
Neural Mechanisms of Actions of Transcranial Electrical Stimulation By Kohitij Kar A Dissertation submitted to the Graduate School-Newark Rutgers, The State University of New Jersey in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Behavioral and Neural Sciences written under the direction of Bart Krekelberg and approved by ________________________ ________________________ ________________________ ________________________ ________________________ Newark, New Jersey January 2015 ©2014 Kohitij Kar ALL RIGHTS RESERVED Abstract of the Dissertation Neural Mechanisms of Actions of Transcranial Electrical Stimulation By Kohitij Kar Dissertation Director: Bart Krekelberg There is considerable evidence for clinical and behavioral efficacy of transcranial electrical stimulation (tES). The effects range from suppressing Parkinsonian tremors to augmenting human learning and memory. Despite widespread use, the neurobiological mechanisms of action of tES on the intact human brain are unclear. In the work presented in this thesis, I have taken a multi-methodological approach to probe tES mechanisms. First, I studied the electric field spread induced by the application of tES, behaviorally. Second, I examined the behavioral effects of tES on human motion perception. I observed that tES (10 Hz, 0.5 mA) applied over area hMT+ (a brain area specialized in processing visual motion) attenuates motion adaptation. This result led to the hypothesis that tES-induced membrane voltage modulations reduce adaptation in motion-selective neurons. Finally, I tested this hypothesis by directly measuring tES-induced neural activity changes in a homologous area in the macaque brain (area MT). Tuning curve estimates of macaque MT neurons showed that tES attenuated the effects of visual motion adaptation on tuning amplitude and width. -
Cortical Mechanisms of Adaptation in Auditory Processing
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Cortical Mechanisms Of Adaptation In Auditory Processing Ryan Natan University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Neuroscience and Neurobiology Commons Recommended Citation Natan, Ryan, "Cortical Mechanisms Of Adaptation In Auditory Processing" (2017). Publicly Accessible Penn Dissertations. 2498. https://repository.upenn.edu/edissertations/2498 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2498 For more information, please contact [email protected]. Cortical Mechanisms Of Adaptation In Auditory Processing Abstract Adaptation is computational strategy that underlies sensory nervous systems’ ability to accurately encode stimuli in various and dynamic contexts and shapes how animals perceive their environment. Many questions remain concerning how adaptation adjusts to particular stimulus features and its underlying mechanisms. In Chapter 2, we tested how neurons in the primary auditory cortex adapt to changes in stimulus temporal correlation. We used chronically implanted tetrodes to record neuronal spiking in rat primary auditory cortex during exposure to custom made dynamic random chord stimuli exhibiting different levels of temporal correlation. We estimated linear non-linear model for each neuron at each temporal correlation level, finding that neurons compensate for temporal correlation changes through gain-control adaptation. This experiment extends our understanding of how complex stimulus statistics are encoded in the auditory nervous system. In Chapter 3 and 4, we tested how interneurons are involved in adaptation by optogenetically suppressing parvalbumin-positive (PV) and somatostatin- positive (SOM) interneurons during tone train stimuli and using silicon probes to record neuronal spiking in mouse primary auditory cortex. -
Fast Hebbian Plasticity Explains Working Memory and Neural Binding
bioRxiv preprint doi: https://doi.org/10.1101/334821; this version posted May 30, 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. Fast Hebbian Plasticity explains Working Memory and Neural Binding Florian Fiebig1, Pawel Herman1, and Anders Lansner1,2 1 Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 10044 Stockholm, Sweden, 2 Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden Abstract We have extended a previous spiking neural network model of prefrontal cortex with fast Hebbian plasticity to also include interactions between short-term and long-term memory stores. We investigated how prefrontal cortex could bind and maintain multi-modal long-term memory representations by simulating three cortical patches in macaque brain, i.e. in prefrontal cortex together with visual and auditory temporal cortex. Our simulation results demonstrate how simultaneous brief multi-modal activation of items in long- term memory could build a temporary joint cell assembly linked via prefrontal cortex. The latter can then activate spontaneously and thereby reactivate the associated long-term representations. Cueing one long-term memory item rapidly pattern completes the associated un-cued item via prefrontal cortex. The short-term memory network flexibly updates as new stimuli arrive thereby gradually over- writing older representation. In a wider context, this working memory model suggests a novel solution to the “binding problem”, a long-standing and fundamental issue in cognitive neuroscience. -
Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
ORIGINAL RESEARCH published: 07 April 2016 doi: 10.3389/fnana.2016.00037 Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware James C. Knight 1*, Philip J. Tully 2, 3, 4, Bernhard A. Kaplan 5, Anders Lansner 2, 3, 6 and Steve B. Furber 1 1 Advanced Processor Technologies Group, School of Computer Science, University of Manchester, Manchester, UK, 2 Department of Computational Biology, Royal Institute of Technology, Stockholm, Sweden, 3 Stockholm Brain Institute, Karolinska Institute, Stockholm, Sweden, 4 Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK, 5 Department of Visualization and Data Analysis, Zuse Institute Berlin, Berlin, Germany, 6 Department of Numerical analysis and Computer Science, Stockholm University, Stockholm, Sweden SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of Edited by: biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Wolfram Schenck, University of Applied Sciences Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework Bielefeld, Germany for modeling the interaction of different plasticity mechanisms using spiking neurons. Reviewed by: However, it can be computationally expensive to simulate large networks with BCPNN Guy Elston, learning since it requires multiple state variables for each synapse, each of which needs Centre for Cognitive Neuroscience, Australia to be updated every simulation time-step. -
Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
Large-Scale simulations of plastic neural networks on neuromorphic hardware Article (Published Version) Knight, James C, Tully, Philip J, Kaplan, Bernhard A, Lansner, Anders and Furber, Steve B (2016) Large-Scale simulations of plastic neural networks on neuromorphic hardware. Frontiers in Neuroanatomy, 10. a37. ISSN 1662-5129 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/69140/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk ORIGINAL RESEARCH published: 07 April 2016 doi: 10.3389/fnana.2016.00037 Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware James C. -
Whole Brain Emulation a Roadmap
Whole Brain Emulation A Roadmap (2008) Technical Report #2008‐3 Anders Sandberg* Nick Bostrom Future of Humanity Institute Faculty of Philosophy & James Martin 21st Century School Oxford University CITE: Sandberg, A. & Bostrom, N. (2008): Whole Brain Emulation: A Roadmap, Technical Report #2008‐3, Future of Humanity Institute, Oxford University URL: www.fhi.ox.ac.uk/reports/2008‐3.pdf (*) Corresponding author: [email protected] In memoriam: Bruce H. McCormick (1930 – 2007) 2 Contents Whole Brain Emulation............................................................................................................................1 A Roadmap ................................................................................................................................................1 In memoriam: Bruce H. McCormick (1930 – 2007)...........................................................................2 Contents..................................................................................................................................................3 Introduction ...........................................................................................................................................5 Thanks to............................................................................................................................................6 The concept of brain emulation..........................................................................................................7 Emulation and simulation...............................................................................................................7 -
An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity
Research Article: Research Article: New Research | Cognition and Behavior An Indexing Theory for Working Memory based on Fast Hebbian Plasticity https://doi.org/10.1523/ENEURO.0374-19.2020 Cite as: eNeuro 2020; 10.1523/ENEURO.0374-19.2020 Received: 17 September 2019 Revised: 17 January 2020 Accepted: 27 January 2020 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.eneuro.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2020 Fiebig et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 An Indexing Theory for Working Memory based on Fast Hebbian 2 Plasticity 3 Abbreviated Title: Fast Hebbian Indexing Theory for WM 4 Authors: Florian Fiebig1, Pawel Herman1, and Anders Lansner1,2,* 5 1. Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 6 10044 Stockholm, Sweden, 7 2. Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden 8 * Corresponding Author: Anders Lansner, Department of Computational Science and Technology, Royal Institute 9 of Technology (KTH), Stockholm, Lindstedtsvägen 24, 100 44,