DOCSLIB.ORG

Constructive Role of Plasticity Rules in Reservoir Computing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Constructive Role of Plasticity Rules in Reservoir Computing Constructive role of plasticity rules in reservoir computing Guillermo Gabriel Barrios Morales Master’s Thesis Master’s degree in Physics of Complex Systems at the UNIVERSITAT DE LES ILLES BALEARS Academic year 2018-2019 Date: 13/09/2019 UIB Master’s Thesis Supervisors: Miguel C. Soriano and Claudio R. Mirasso “Our treasure lies in the beehives of our knowledge. We are perpetually on our way thither, being by nature winged insects and honey gatherers of the mind. The only thing that lies close to our heart is the desire to bring something home to the hive.” — Friedrich Nietzsche Constructive role of plasticity rules in reservoir computing G. Barrios Morales ABSTRACT Over the last 15 years, Reservoir Computing (RC) has emerged as an appealing ap- proach in Machine Learning, combining the high computational capabilities of Recur- rent Neural Networks with a fast and easy training. By mapping the inputs into a high-dimensional space of non-linear neurons, this class of algorithms have shown their utility in a wide range of tasks from speech recognition to time series prediction. With their popularity on the rise, new works have pointed to the possibility of RC as an ex- isting learning paradigm within the actual brain. Likewise, successful implementation of biologically based plasticity rules into RC artificial networks has boosted the perfor- mance of the original models. Within these nature-inspired approaches, most research lines focus on improving the performance achieved by previous works on prediction or classification tasks. In this thesis however, we will address the problem from a different perspective: instead on focusing on the results of the improved models, we will analyze the role of plasticity rules on the changes that lead to a better performance. To this end, we implement synaptic and non-synaptic plasticity rules in a standard Echo State Network , which is a paradigmatic example of an RC model. Testing on temporal series prediction tasks, we show evidence that improved performance in all plastic models may be linked to a decrease in spatio-temporal correlations in the reservoir, as well as a significant increase on individual neurons ability to separate similar inputs in their activity space. From the perspective of the reservoir dynamics, optimal performance is suggested to occur at the edge of instability. This is a hypothesis previously suggested in literature, but we hope to provide new insight on the matter through the study of different stages on the plastic learning. Finally, we show that it is possible to combine different forms of plasticity (namely synaptic and non-synaptic rules) to further improve the performance on prediction tasks, obtaining better results than those achieved with single-plasticity models. Key words : Reservoir Computing, plasticity rules, Hebbian learning, intrinsic plastic- ity, temporal series prediction. 2 Constructive role of plasticity rules in reservoir computing G. Barrios Morales Contents 1 Introduction 4 1.1 On the shoulders of giants: a brief historical perspective. ........ 4 1.2 Motivation and objectives of the thesis. ................. 9 2 Theoretical framework 11 2.1 The ESN architecture. ........................... 11 2.2 The role of hyper-parameters. ....................... 12 2.3 Training a simple ESN. ........................... 14 2.4 Neural plasticity: biological and artificial implementations. ....... 17 2.4.1 Plasticity in the brain. ...................... 17 2.4.2 Plasticity in Echo State Networks. ................. 18 2.5 Prediction of a chaotic time series. .................... 22 2.6 Memory capacity task. ........................... 24 3 Results 26 3.1 Hyper-parameter optimization. ...................... 26 3.2 Performance in prediction task with MG-17. ............... 27 3.3 Performance in prediction task with MG-30. ............... 31 3.4 Performance in memory capacity task. .................. 34 3.5 Understanding the effects of plasticity. .................. 35 4 Conclusions 44 3 Constructive role of plasticity rules in reservoir computing G. Barrios Morales 1 Introduction 1.1 On the shoulders of giants: a brief historical perspective. From the first bird-inspired “flying machines” of Leonardo da Vinci to the latest ad- vances in artificial photosynthesis, humankind has constantly sought to mimic nature in order to solve complex problems. It is therefore not surprising that the dawn of Ma- chine Learning (ML) and Artificial Neural Networks (ANN) was also characterized by the idea of emulating the functionalities and characteristics of the human brain. Within his book The Organization of Behavior, Donald Hebb proposed in 1949 a neurophys- iological model of neuron interaction that attempted to explain the way associative learning takes place [1]. Theorizing on the basis of synaptic plasticity, Hebb suggested that the simultaneous activation of cells would lead to the reinforcement of the synapses involved. Today known as Hebbian theory1, its main postulate reads: “When an axon of cell A is near enough to excite cell B or repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” [1] Swiftly, Hebbian theory was taken by neurophysiologists and early brain modelers as the foundation upon which to build the first working artificial neural network. In 1950, a young Nat Rochester at the IBM research lab embarked in the project of modeling an artificial cell assembly following Hebb’s rules [4]. However, he would soon be discouraged by an obvious flaw in Hebb’s initial theory: as connection strength increases with the learning process, neural activity eventually spreads across the whole assembly, saturating the network. Further attempts were made to find the “additional rule” which could prevent the overgrowing of connections, but none of them proved successful, and the project was soon consigned to oblivion. It wouldn’t be until 1957 when Frank Rosenblatt, who had previously read The Or- ganization of Behavior and sought to find a more “model-friendly” version of Hebb’s assembly, came with a solution: the Perceptron, the first example of a Feed Forward Neural Network (FFNN) [5]. Dismissing the idea of an homogeneous mass of cells, 1As Hebb himself would recognized later, the idea of associative learning based on alterations in the strength of the connections between neurons was far from new [2]. Today, the merit of the theory is commonly conceded to Tanzi [3], although other authors claim that similar ideas had been already discussed by Alexander Bain even before [4]. 4 Constructive role of plasticity rules in reservoir computing G. Barrios Morales Rosenblatt introduced three types of units within the network: sensory units (S-units), to which impulses are transmitted when they are activated; association units (A-units), integrating the signals from the S-units and transmitting it to the R-units; and response units (R-units), which produce the output, becoming active if the total signal coming from the A-units exceeds a certain threshold. The original model of Rosenblatt even included inhibitory connections between the S and R-units, and the A-units, becoming a pioneer work in the implementation of feed-back connections within an ANN. The S, A and R sets in the original scheme, correspond today to what is usually known as in- put, hidden and output layer in a FFNN. Mathematically, the output of the perceptron is computed as: 1 if w · x + b > 0 f(x) = (1) 0 otherwise where w · x is the dot product of the input x with the weight vector w and b is a bias term that acts like a moving threshold. In modern FFNNs, the step function is usually substituted by a non-linearity ϕ (w · x + b) which receives the name of activation function. The basic scheme of an artificial neuron in the current notation of FFNNs together with its biological association is depicted in Fig.1. Figure 1: Architecture of a basic artificial neuron. Figure credit to Greezes [6]. Being computationally more applicable than the original ideas of Hebb, Rosenblatt paved the way that would progressively detach ML from its biological inspiration, as he himself would state in the following lines introducing the Perceptron: “The perceptron is designed to illustrate some of the fundamental properties of intelligent systems in general, without becoming too deeply enmeshed in 5 Constructive role of plasticity rules in reservoir computing G. Barrios Morales the special, and frequently unknown, conditions which hold for particular biological organisms.” [5] Despite the initial excitement in the promising new model, it was quickly proved that perceptrons could not be trained to recognize many classes of patterns. In fact, they were only capable of learning linearly separable patterns, becoming a linear classifier. This was shown in 1969 by Marvin Minsky and Seymour Papert in a famous book entitled Perceptrons, where they stated that it was impossible for this type of network to learn an XOR function [7]. In their book, the authors already foresaw the need for Multilayer Perceptrons (MLP) to tackle non-linear classification problems, but the lack of a suitable learning algorithm lead to the first of the AI winters [8], with neural network research stagnating for many years. The solution to this problem came around 1974 through what today are widely known as Backpropagation algorithms. Backpropagation was derived by multiple researchers in the early 60’s and implemented to run on computers as early as 1970 by Seppo Linnainmaa [9], but it was Paul Werbos —back then a PhD student at Harvard Uni- versity— the first one to propose that it could be used to effectively train MLPs [10]. Understood as a supervised learning method in multilayer networks, backpropagation aims to adjust the internal weights in each layer so to minimize the error or loss function at the output, using a gradient-descent approach based on the chain rule to propagate the error from the outer to the inner layers.
Load more

Recommended publications
  • Electromagnetic Field and TGF-Β Enhance the Compensatory
    www.nature.com/scientificreports OPEN Electromagnetic feld and TGF‑β enhance the compensatory plasticity after sensory nerve injury in cockroach Periplaneta americana Milena Jankowska1, Angelika Klimek1, Chiara Valsecchi2, Maria Stankiewicz1, Joanna Wyszkowska1* & Justyna Rogalska1 Recovery of function after sensory nerves injury involves compensatory plasticity, which can be observed in invertebrates. The aim of the study was the evaluation of compensatory plasticity in the cockroach (Periplaneta americana) nervous system after the sensory nerve injury and assessment of the efect of electromagnetic feld exposure (EMF, 50 Hz, 7 mT) and TGF‑β on this process. The bioelectrical activities of nerves (pre‑and post‑synaptic parts of the sensory path) were recorded under wind stimulation of the cerci before and after right cercus ablation and in insects exposed to EMF and treated with TGF‑β. Ablation of the right cercus caused an increase of activity of the left presynaptic part of the sensory path. Exposure to EMF and TGF‑β induced an increase of activity in both parts of the sensory path. This suggests strengthening efects of EMF and TGF‑β on the insect ability to recognize stimuli after one cercus ablation. Data from locomotor tests proved electrophysiological results. The takeover of the function of one cercus by the second one proves the existence of compensatory plasticity in the cockroach escape system, which makes it a good model for studying compensatory plasticity. We recommend further research on EMF as a useful factor in neurorehabilitation. Injuries in the nervous system caused by acute trauma, neurodegenerative diseases or even old age are hard to reverse and represent an enormous challenge for modern medicine.
    [Show full text]
  • 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.
    [Show full text]
  • New Clues for the Cerebellar Mechanisms of Learning
    REVIEW Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning Egidio D’Angelo1,2 & Lisa Mapelli1,3 & Claudia Casellato 5 & Jesus A. Garrido1,4 & Niceto Luque 4 & Jessica Monaco2 & Francesca Prestori1 & Alessandra Pedrocchi 5 & Eduardo Ros 4 Abstract The cerebellum is involved in learning and memory it can easily cope with multiple behaviors endowing therefore of sensory motor skills. However, the way this process takes the cerebellum with the properties needed to operate as an place in local microcircuits is still unclear. The initial proposal, effective generalized forward controller. casted into the Motor Learning Theory, suggested that learning had to occur at the parallel fiber–Purkinje cell synapse under Keywords Cerebellum . Distributed plasticity . Long-term supervision of climbing fibers. However, the uniqueness of this synaptic plasticity . LTP . LTD . Learning . Memory mechanism has been questioned, and multiple forms of long- term plasticity have been revealed at various locations in the cerebellar circuit, including synapses and neurons in the gran- Introduction ular layer, molecular layer and deep-cerebellar nuclei. At pres- ent, more than 15 forms of plasticity have been reported. There The cerebellum is involved in the acquisition of procedural mem- has been a long debate on which plasticity is more relevant to ory, and several attempts have been done at linking cerebellar specific aspects of learning, but this question turned out to be learning to the underlying neuronal circuit mechanisms.
    [Show full text]
  • Article Role of Delayed Nonsynaptic Neuronal Plasticity in Long-Term Associative Memory
    Current Biology 16, 1269–1279, July 11, 2006 ª2006 Elsevier Ltd All rights reserved DOI 10.1016/j.cub.2006.05.049 Article Role of Delayed Nonsynaptic Neuronal Plasticity in Long-Term Associative Memory Ildiko´ Kemenes,1 Volko A. Straub,1,3 memory, and we describe how this information can be Eugeny S. Nikitin,1 Kevin Staras,1,4 Michael O’Shea,1 translated into modified network and behavioral output. Gyo¨ rgy Kemenes,1,2,* and Paul R. Benjamin1,2,* 1 Sussex Centre for Neuroscience Introduction Department of Biological and Environmental Sciences School of Life Sciences It is now widely accepted that nonsynaptic as well as University of Sussex synaptic plasticity are substrates for long-term memory Falmer, Brighton BN1 9QG [1–4]. Although information is available on how nonsy- United Kingdom naptic plasticity can emerge from cellular processes active during learning [3], far less is understood about how it is translated into persistently modified behavior. Summary There are, for instance, important unanswered ques- tions regarding the timing and persistence of nonsynap- Background: It is now well established that persistent tic plasticity and the relationship between nonsynaptic nonsynaptic neuronal plasticity occurs after learning plasticity and changes in synaptic output. In this paper and, like synaptic plasticity, it can be the substrate for we address these important issues by looking at an long-term memory. What still remains unclear, though, example of learning-induced nonsynaptic plasticity is how nonsynaptic plasticity contributes to the altered (somal depolarization), its onset and persistence, and neural network properties on which memory depends. its effects on synaptically mediated network activation Understanding how nonsynaptic plasticity is translated in an experimentally tractable molluscan model system.
    [Show full text]
  • Long-Term Plasticity of Intrinsic Excitability
    Downloaded from learnmem.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Review Long-Term Plasticity of Intrinsic Excitability: Learning Rules and Mechanisms Gae¨l Daoudal and Dominique Debanne1 Institut National de la Sante´Et de la Recherche Me´dicale UMR464 Neurobiologie des Canaux Ioniques, Institut Fe´de´ratif Jean Roche, Faculte´deMe´decine Secteur Nord, Universite´d’Aix-Marseille II, 13916 Marseille, France Spatio-temporal configurations of distributed activity in the brain is thought to contribute to the coding of neuronal information and synaptic contacts between nerve cells could play a central role in the formation of privileged pathways of activity. Synaptic plasticity is not the exclusive mode of regulation of information processing in the brain, and persistent regulations of ionic conductances in some specialized neuronal areas such as the dendrites, the cell body, and the axon could also modulate, in the long-term, the propagation of neuronal information. Persistent changes in intrinsic excitability have been reported in several brain areas in which activity is elevated during a classical conditioning. The role of synaptic activity seems to be a determinant in the induction, but the learning rules and the underlying mechanisms remain to be defined. We discuss here the role of synaptic activity in the induction of intrinsic plasticity in cortical, hippocampal, and cerebellar neurons. Activation of glutamate receptors initiates a long-term modification in neuronal excitability that may represent a parallel, synergistic substrate for learning and memory. Similar to synaptic plasticity, long-lasting intrinsic plasticity appears to be bidirectional and to express a certain level of input or cell specificity.
    [Show full text]
  • Diverse Impact of Acute and Long-Term Extracellular Proteolytic Activity on Plasticity of Neuronal Excitability
    REVIEW published: 10 August 2015 doi: 10.3389/fncel.2015.00313 Diverse impact of acute and long-term extracellular proteolytic activity on plasticity of neuronal excitability Tomasz Wójtowicz 1*, Patrycja Brzda, k 2 and Jerzy W. Mozrzymas 1,2 1 Laboratory of Neuroscience, Department of Biophysics, Wroclaw Medical University, Wroclaw, Poland, 2 Department of Animal Physiology, Institute of Experimental Biology, Wroclaw University, Wroclaw, Poland Learning and memory require alteration in number and strength of existing synaptic connections. Extracellular proteolysis within the synapses has been shown to play a pivotal role in synaptic plasticity by determining synapse structure, function, and number. Although synaptic plasticity of excitatory synapses is generally acknowledged to play a crucial role in formation of memory traces, some components of neural plasticity are reflected by nonsynaptic changes. Since information in neural networks is ultimately conveyed with action potentials, scaling of neuronal excitability could significantly enhance or dampen the outcome of dendritic integration, boost neuronal information storage capacity and ultimately learning. However, the underlying mechanism is poorly understood. With this regard, several lines of evidence and our most recent study support a view that activity of extracellular proteases might affect information processing Edited by: Leszek Kaczmarek, in neuronal networks by affecting targets beyond synapses. Here, we review the most Nencki Institute, Poland recent studies addressing the impact of extracellular proteolysis on plasticity of neuronal Reviewed by: excitability and discuss how enzymatic activity may alter input-output/transfer function Ania K. Majewska, University of Rochester, USA of neurons, supporting cognitive processes. Interestingly, extracellular proteolysis may Anna Elzbieta Skrzypiec, alter intrinsic neuronal excitability and excitation/inhibition balance both rapidly (time of University of Exeter, UK minutes to hours) and in long-term window.
    [Show full text]
  • Plasticity in the Olfactory System: Lessons for the Neurobiology of Memory D
    REVIEW I Plasticity in the Olfactory System: Lessons for the Neurobiology of Memory D. A. WILSON, A. R. BEST, and R. M. SULLIVAN Department of Zoology University of Oklahoma We are rapidly advancing toward an understanding of the molecular events underlying odor transduction, mechanisms of spatiotemporal central odor processing, and neural correlates of olfactory perception and cognition. A thread running through each of these broad components that define olfaction appears to be their dynamic nature. How odors are processed, at both the behavioral and neural level, is heavily depend- ent on past experience, current environmental context, and internal state. The neural plasticity that allows this dynamic processing is expressed nearly ubiquitously in the olfactory pathway, from olfactory receptor neurons to the higher-order cortex, and includes mechanisms ranging from changes in membrane excitabil- ity to changes in synaptic efficacy to neurogenesis and apoptosis. This review will describe recent findings regarding plasticity in the mammalian olfactory system that are believed to have general relevance for understanding the neurobiology of memory. NEUROSCIENTIST 10(6):513–524, 2004. DOI: 10.1177/1073858404267048 KEY WORDS Olfaction, Plasticity, Memory, Learning, Perception Odor perception is situational, contextual, and ecologi- ory for several reasons. First, the olfactory system is cal. Odors are not stored in memory as unique entities. phylogenetically highly conserved, and memory plays a Rather, they are always interrelated with other sensory critical role in many ecologically significant odor- perceptions . that happen to coincide with them. guided behaviors. Thus, many different animal models, ranging from Drosophila to primates, can be taken —Engen (1991, p 86–87) advantage of to address specific experimental questions.
    [Show full text]
  • Mechanisms of Experience-Dependent Prevention of Plasticity in Visual Circuits
    Georgia State University ScholarWorks @ Georgia State University Neuroscience Institute Dissertations Neuroscience Institute Summer 8-12-2014 Mechanisms of Experience-dependent Prevention of Plasticity in Visual Circuits Timothy Balmer Follow this and additional works at: https://scholarworks.gsu.edu/neurosci_diss Recommended Citation Balmer, Timothy, "Mechanisms of Experience-dependent Prevention of Plasticity in Visual Circuits." Dissertation, Georgia State University, 2014. https://scholarworks.gsu.edu/neurosci_diss/13 This Dissertation is brought to you for free and open access by the Neuroscience Institute at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Neuroscience Institute Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. MECHANISMS OF EXPERIENCE-DEPENDENT PREVENTION OF PLASTICITY IN VISUAL CIRCUITS by TIMOTHY S. BALMER Under the Direction of Sarah L. Pallas ABSTRACT Development of brain function is instructed by both genetically-determined processes (nature) and environmental stimuli (nurture). The relative importance of nature and nurture is a major question in developmental neurobiology. In this dissertation, I investigated the role of visual experience in the development and plasticity of the visual pathway. Each neuron that receives visual input responds to a specific area of the visual field- their receptive field (RF). Developmental refinement reduces RF size and underlies visual acuity, which is important for survival. By rearing Syrian hamsters ( Mesocricetus auratus ) in constant darkness (dark rearing, DR) from birth, I investigated the role of visual experience in RF refinement and plasticity. Previous work in this lab has shown that developmental refinement of RFs occurs in the absence of visual experience in the superior colliculus (SC), but that RFs unrefine and thus enlarge in adulthood during chronic DR.
    [Show full text]
  • Integrated Plasticity at Inhibitory and Excitatory Synapses in the Cerebellar Circuit
    REVIEW published: 05 May 2015 doi: 10.3389/fncel.2015.00169 Integrated plasticity at inhibitory and excitatory synapses in the cerebellar circuit Lisa Mapelli 1,2†, Martina Pagani 1,3†, Jesus A. Garrido 4,5 and Egidio D’Angelo 1,4* 1 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, 2 Museo Storico Della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy, 3 Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland, 4 Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, 5 Department of Computer Architecture and Technology, University of Granada, Granada, Spain The way long-term potentiation (LTP) and depression (LTD) are integrated within the different synapses of brain neuronal circuits is poorly understood. In order to progress beyond the identification of specific molecular mechanisms, a system in which multiple forms of plasticity can be correlated with large-scale neural processing is required. In this paper we take as an example the cerebellar network, in which extensive investigations have revealed LTP and LTD at several excitatory and inhibitory synapses. Cerebellar LTP and LTD occur in all three main cerebellar subcircuits (granular layer, molecular layer, deep cerebellar nuclei) and correspondingly regulate the function of their three main neurons: granule cells (GrCs), Purkinje cells (PCs) and deep cerebellar nuclear (DCN) Edited by: cells. All these neurons, in addition to be excited, are reached by feed-forward and feed- Andrea Barberis, back inhibitory connections, in which LTP and LTD may either operate synergistically Fondazione Istituto Italiano di Tecnologia, Italy or homeostatically in order to control information flow through the circuit.
    [Show full text]
  • 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.
    [Show full text]
  • Circuit Mechanisms of Memory Formation
    Neural Plasticity Circuit Mechanisms of Memory Formation Guest Editors: Björn M. Kampa, Anja Gundlfinger, Johannes J. Letzkus, and Christian Leibold Circuit Mechanisms of Memory Formation Neural Plasticity Circuit Mechanisms of Memory Formation Guest Editors: Bjorn¨ M. Kampa, Anja Gundlfinger, Johannes J. Letzkus, and Christian Leibold Copyright © 2011 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in volume 2011 of “Neural Plasticity.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board Robert Adamec, Canada Anthony Hannan, Australia Vilayanur S. Ramachandran, USA Shimon Amir, Canada George W. Huntley, USA Kerry J. Ressler, USA Michel Baudry, USA Yuji Ikegaya, Japan Susan J. Sara, France Michael S. Beattie, USA Leszek Kaczmarek, Poland Timothy Schallert, USA Clive Raymond Bramham, Norway Jeansok J. Kim, USA Menahem Segal, Israel Anna Katharina Braun, Germany Eric Klann, USA Panagiotis Smirniotis, USA Sumantra Chattarji, India Małgorzata Kossut, Poland Ivan Soltesz, USA Robert Chen, Canada Frederic Libersat, Israel Michael G. Stewart, UK David Diamond, USA Stuart C. Mangel, UK Naweed I. Syed, Canada M. B. Dutia, UK Aage R. Møller, USA Donald A. Wilson, USA Richard Dyck, Canada Diane K. O’Dowd, USA J. R. Wolpaw, USA Zygmunt Galdzicki, USA A. Pascual-Leone, USA Chun-Fang Wu, USA PrestonE.Garraghty,USA Maurizio Popoli, Italy J. M. Wyss, USA Paul E. Gold, USA Bruno Poucet, France Lin Xu, China Manuel B. Graeber, Australia Lucas Pozzo-Miller, USA Min Zhuo, Canada Contents Circuit Mechanisms of Memory Formation,Bjorn¨ M.
    [Show full text]
  • Title: How Can Memories Last for Days, Years, Or a Lifetime? Proposed Mechanisms for Maintaining Synaptic Potentiation and Memory
    Smolen et al. Title: How Can Memories Last for Days, Years, or a Lifetime? Proposed Mechanisms for Maintaining Synaptic Potentiation and Memory Authors: Paul Smolen, Douglas A. Baxter, and John H. Byrne Laboratory of Origin: Department of Neurobiology and Anatomy W. M. Keck Center for the Neurobiology of Learning and Memory McGovern Medical School of the University of Texas Health Science Center at Houston Houston, Texas 77030 Running Title: Proposed Mechanisms to Maintain LTP and Memory Correspondence Address: Paul D. Smolen Department of Neurobiology and Anatomy W.M. Keck Center for the Neurobiology of Learning and Memory McGovern Medical School of the University of Texas Health Science Center at Houston Houston, TX 77030 E-mail: [email protected] Vo ice: (713) 500-5601 FAX: (713) 500-0623 1 Smolen et al. Abstract With memory encoding reliant on persistent changes in the properties of synapses, a key question is how can memories be maintained from days to months or a lifetime given molecular turnover? It is likely that positive feedback loops are necessary to persistently maintain the strength of synapses that participate in encoding. Such feedback may occur within signal-transduction cascades and/or the regulation of translation, and it may occur within specific subcellular compartments or within neuronal networks. Not surprisingly, numerous positive feedback loops have been proposed. Some posited loops operate at the level of biochemical signal transduction cascades, such as persistent activation of Ca2+/calmodulin kinase II (CaMKII) or protein kinase M ζ. Another level consists of feedback loops involving transcriptional, epigenetic and translational pathways, and autocrine actions of growth factors such as BDNF.
    [Show full text]