Fast Hebbian Plasticity Explains Working Memory and Neural Binding
Total Page:16
File Type:pdf, Size:1020Kb
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. -
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. -
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. -
Time-Frequency Based Phase-Amplitude Coupling
www.nature.com/scientificreports OPEN Time-Frequency Based Phase- Amplitude Coupling Measure For Neuronal Oscillations Received: 17 September 2018 Tamanna T. K. Munia & Selin Aviyente Accepted: 9 August 2019 Oscillatory activity in the brain has been associated with a wide variety of cognitive processes including Published: xx xx xxxx decision making, feedback processing, and working memory. The high temporal resolution provided by electroencephalography (EEG) enables the study of variation of oscillatory power and coupling across time. Various forms of neural synchrony across frequency bands have been suggested as the mechanism underlying neural binding. Recently, a considerable amount of work has focused on phase- amplitude coupling (PAC)– a form of cross-frequency coupling where the amplitude of a high frequency signal is modulated by the phase of low frequency oscillations. The existing methods for assessing PAC have some limitations including limited frequency resolution and sensitivity to noise, data length and sampling rate due to the inherent dependence on bandpass fltering. In this paper, we propose a new time-frequency based PAC (t-f PAC) measure that can address these issues. The proposed method relies on a complex time-frequency distribution, known as the Reduced Interference Distribution (RID)-Rihaczek distribution, to estimate both the phase and the envelope of low and high frequency oscillations, respectively. As such, it does not rely on bandpass fltering and possesses some of the desirable properties of time-frequency distributions such as high frequency resolution. The proposed technique is frst evaluated for simulated data and then applied to an EEG speeded reaction task dataset. The results illustrate that the proposed time-frequency based PAC is more robust to varying signal parameters and provides a more accurate measure of coupling strength. -
The Scarab 2010
THE SCARAB 2010 28th Edition Oklahoma City University 2 THE SCARAB 2010 28th Edition Oklahoma City University’s Presented by Annual Anthology of Prose, Sigma Tau Delta, Poetry, and Artwork Omega Phi Chapter Editors: Ali Cardaropoli, Kenneth Kimbrough, Emma Johnson, Jake Miller, and Shana Barrett Dr. Terry Phelps, Sponsor Copyright © The Scarab 2010 All rights reserved 3 The Scarab is published annually by the Oklahoma City University chapter of Sigma Tau Delta, the International English Honor Society. Opinions and beliefs expressed herein do not necessarily reflect those of the university, the chapter, or the editors. Submissions are accepted from students, faculty, staff, and alumni. Address all correspondence to The Scarab c/o Dr. Terry Phelps, 2501 N. Blackwelder, Oklahoma City, OK 73106, or e-mail [email protected]. The Scarab is not responsible for returning submitted work. All submissions are subject to editing. 4 THE SCARAB 2010 POETRY NONFICTION We Hold These Truths By Neilee Wood by Spencer Hicks ................................. 8 Fruition ...................................................... 41 It‘s Too Early ............................................ 42 September 1990 Skin ........................................................... 42 by Sheray Franklin .............................. 10 By Abigail Keegan Fearless with Baron Birding ...................................................... 43 by Terre Cooke-Chaffin ........................ 12 By Daniel Correa Inevitable Ode to FDR .............................................. -
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. -
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. -
Jarosek Copy.Fm
Cybernetics and Human Knowing. Vol. 27 (2020), no. 3, pp. 33–63 Knowing How to Be: Imitation, the Neglected Axiom Stephen Laszlo Jarosek1 The concept of imitation has been around for a very long time, and many conversations have been had about it, from Plato and Aristotle to Piaget and Freud. Yet despite this pervasive acknowledgement of its relevance in areas as diverse as memetics, culture, child development and language, there exists little appreciation of its relevance as a fundamental principle in the semiotic and life sciences. Reframing imitation in the context of knowing how to be, within the framework of semiotic theory, can change this, thus providing an interpretation of paradigmatic significance. However, given the difficulty of establishing imitation as a fundamental principle after all these centuries since Plato, I turn the question around and approach it from a different angle. If imitation is to be incorporated into semiotic theory and the Peircean categories as axiomatic, then what pathologies manifest when imitation is disabled or compromised? I begin by reviewing the reasons for regarding imitation as a fundamental principle. I then review the evidence with respect to autism and schizophrenia as imitation deficit. I am thus able to consolidate my position that imitation and knowing how to be are integral to agency and pragmatism (semiotic theory), and should be embraced within an axiomatic framework for the semiotic and life sciences. Keywords: autism; biosemiotics; imitation; neural plasticity; Peirce; pragmatism The concept of imitation has been around for a very long time, and many conversations have been had about it, from Plato and Aristotle to Piaget and Freud. -
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. -
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. -
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. -
Wikipedia.Org/W/Index.Php?Title=Neural Oscillation&Oldid=898604092"
Neural oscillation Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity. Neural oscillations were observed by researchers as early as 1924 (by Hans Berger). More than 50 years later, intrinsic oscillatory behavior was encountered in vertebrate neurons, but its functional role is still not fully understood.[1] The possible roles of neural oscillations include feature binding, information transfer mechanisms and the generation of rhythmic motor output. Over the last decades more insight has been gained, especially with advances in brain imaging. A major area of research in neuroscience involves determining how oscillations are generated and what their roles are. Oscillatory activity in the brain is widely observed at different levels of organization and is thought to play a key role in processing neural information.