Associative Memory-I: Storing Patterns
|
|
- Oliver Sims
- 6 years ago
- Views:
Transcription
1 Printed from the Mathematica Help Browser 1 «1 of 11 Associative Memory-I: Storing Patterns Learning and recalling memories are important to animals' survival. Navigational memory is important for locating food. Odor memory is important for determining if food is safe to eat. «2 of 11
2 2 Printed from the Mathematica Help Browser Storing Patterns Table Projector Chairs Handouts University Classroom Students White board Some types of memories can be broken up into their constituent elements. A basic element of learning is pattern storage and retrieval. Patterns can consist of sensory, motor, or both elements. «3 of 11
3 Printed from the Mathematica Help Browser 3 Storing Patterns Associatively Table Projector Chairs Handouts University Classroom Students White board Memories can be stored associatively by linking the components together. Associative patterns link elements together. When parts of the pattern are presented, they recruit the other parts, recalling the whole. «4 of 11
4 4 Printed from the Mathematica Help Browser Association with Neurons Populations of neurons can represent the elements in a pattern. Populations of neurons represent elements. Patterns can be stored by synapses in two ways: Æchange in input synaptic strengths Æchange in recurrent synaptic strengths «5 of 11 Association with Neurons
5 Printed from the Mathematica Help Browser 5 Missing elements of a pattern can be recruited by the active elements. Strengthened recurrent synapses can recruit missing elements of a pattern, recalling the original. Associative memory is useful for recalling memories similar to the current situation. «6 of 11 Neurons Associate Place cells respond the same in a maze when all identifying elements are present (full cue) as when most of them are removed (partial cue) [Nakazawa et al, 2002]. After learning to navigate a maze with four identifying elements, mice perform as well when three elements are removed. This can be interpretted as the network performing associative recall. «7 of 11
6 6 Printed from the Mathematica Help Browser Abstract Association: The Hopfield Network The (eight-neuron) Hopfield network is characterized by all-to-all recurrent connectivity and employs a hebbian learning rule to store (eight-bit) binary patterns. The Hopfield network stores patterns by strengthening recurrent synapses among neurons that are in a pattern together. «8 of 11 The Hopfield Network-II
7 Printed from the Mathematica Help Browser 7 Strengthened synapses make some network states more stable than others. The network approaches these stable attractor states. «9 of 11 Neurons Associate with Recurrent Connections Place cells respond less with a partial cue compared to the full cue [Nakazawa et al, 2002]. Genetically eliminating learning at recurrent CA3 synapses (by eliminating NMDARs) damages place cell activity. After learning to navigate a maze with four identifying elements, mutuant mice perform poorly when three elements are removed. «10 of 11
8 8 Printed from the Mathematica Help Browser A More Biological Model-I Our network is characterized by spiking neurons that make random local recurrent connections to each other that employs STDP to store binary patterns. Like the Hopfield network, our network stores patterns by strengthening recurrent synapses among coactive neurons. «11 of 11 A More Biological Model-II Before STDP After STDP Rate(Hz)
9 Printed from the Mathematica Help Browser 9 After STDP stores a pattern, potentiated neurons recall the whole pattern when half of the neurons in the pattern are activated. The network recalls a pattern when a subset of its neurons are activated.
Artificial Neural Networks (Ref: Negnevitsky, M. Artificial Intelligence, Chapter 6)
Artificial Neural Networks (Ref: Negnevitsky, M. Artificial Intelligence, Chapter 6) BPNN in Practice Week 3 Lecture Notes page 1 of 1 The Hopfield Network In this network, it was designed on analogy of
More informationThe storage and recall of memories in the hippocampo-cortical system. Supplementary material. Edmund T Rolls
The storage and recall of memories in the hippocampo-cortical system Supplementary material Edmund T Rolls Oxford Centre for Computational Neuroscience, Oxford, England and University of Warwick, Department
More informationSTDP enhances synchrony in feedforward network
1 1 of 10 STDP enhances synchrony in feedforward network STDP strengthens/weakens synapses driving late/early-spiking cells [Laurent07] Olfactory neurons' spikes phase-lock (~2ms) to a 20Hz rhythm. STDP
More informationModeling of Hippocampal Behavior
Modeling of Hippocampal Behavior Diana Ponce-Morado, Venmathi Gunasekaran and Varsha Vijayan Abstract The hippocampus is identified as an important structure in the cerebral cortex of mammals for forming
More informationCerebral Cortex. Edmund T. Rolls. Principles of Operation. Presubiculum. Subiculum F S D. Neocortex. PHG & Perirhinal. CA1 Fornix CA3 S D
Cerebral Cortex Principles of Operation Edmund T. Rolls F S D Neocortex S D PHG & Perirhinal 2 3 5 pp Ento rhinal DG Subiculum Presubiculum mf CA3 CA1 Fornix Appendix 4 Simulation software for neuronal
More informationNavigation: Inside the Hippocampus
9.912 Computational Visual Cognition Navigation: Inside the Hippocampus Jakob Voigts 3 Nov 2008 Functions of the Hippocampus: Memory and Space Short term memory Orientation Memory consolidation(h.m)
More informationASSOCIATIVE MEMORY AND HIPPOCAMPAL PLACE CELLS
International Journal of Neural Systems, Vol. 6 (Supp. 1995) 81-86 Proceedings of the Neural Networks: From Biology to High Energy Physics @ World Scientific Publishing Company ASSOCIATIVE MEMORY AND HIPPOCAMPAL
More informationRolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press.
Digital Signal Processing and the Brain Is the brain a digital signal processor? Digital vs continuous signals Digital signals involve streams of binary encoded numbers The brain uses digital, all or none,
More informationImproving Associative Memory in a Network of Spiking
Improving Associative Memory in a Network of Spiking Neurons Computing Science and Mathematics School of Natural Sciences University of Stirling Scotland FK9 4LA Thesis submitted for the degree of Doctor
More informationSynaptic plasticity and hippocampal memory
Synaptic plasticity and hippocampal memory Tobias Bast School of Psychology, University of Nottingham tobias.bast@nottingham.ac.uk Synaptic plasticity as the neurophysiological substrate of learning Hebb
More informationLong-term synaptic plasticity. N500, 6 Sept., 2016
Long-term synaptic plasticity N500, 6 Sept., 2016 We just finished describing short-term synaptic changes What about long-term changes? How does the brain store information? Outline Origins of the synaptic
More informationBeyond Vanilla LTP. Spike-timing-dependent-plasticity or STDP
Beyond Vanilla LTP Spike-timing-dependent-plasticity or STDP Hebbian learning rule asn W MN,aSN MN Δw ij = μ x j (v i - φ) learning threshold under which LTD can occur Stimulation electrode Recording electrode
More informationCCEI120- Brain Development and the Effects of Early Deprivation - Handout
CCEI120- Brain Development and the Effects of Early Deprivation - Handout Welcome to CCEI120 Course Objectives: By taking notes on the handout and successfully answering assessment questions, participants
More informationUNINFORMATIVE MEMORIES WILL PREVAIL
virtute UNINFORMATIVE MEMORIES WILL PREVAIL THE STORAGE OF CORRELATED REPRESENTATIONS AND ITS CONSEQUENCES Emilio Kropff SISSA, Trieste di Studi Avanzati Internazionale Superiore - e conoscenza seguir
More informationBasics of Computational Neuroscience: Neurons and Synapses to Networks
Basics of Computational Neuroscience: Neurons and Synapses to Networks Bruce Graham Mathematics School of Natural Sciences University of Stirling Scotland, U.K. Useful Book Authors: David Sterratt, Bruce
More informationSupplementary Figure 1. Basic properties of compound EPSPs at
Supplementary Figure 1. Basic properties of compound EPSPs at hippocampal CA3 CA3 cell synapses. (a) EPSPs were evoked by extracellular stimulation of the recurrent collaterals and pharmacologically isolated
More informationA model of the interaction between mood and memory
INSTITUTE OF PHYSICS PUBLISHING NETWORK: COMPUTATION IN NEURAL SYSTEMS Network: Comput. Neural Syst. 12 (2001) 89 109 www.iop.org/journals/ne PII: S0954-898X(01)22487-7 A model of the interaction between
More informationMolecular and Circuit Mechanisms for Hippocampal Learning
Molecular and Circuit Mechanisms for Hippocampal Learning Susumu Tonegawa 1 and Thomas J. McHugh 1 The hippocampus is crucial for the formation of memories of facts and episodes (Scoville and Milner 1957;
More informationSynaptic Plasticity and Connectivity Requirements to Produce Stimulus-Pair Specific Responses in Recurrent Networks of Spiking Neurons
Synaptic Plasticity and Connectivity Requirements to Produce Stimulus-Pair Specific Responses in Recurrent Networks of Spiking Neurons Mark A. Bourjaily, Paul Miller* Department of Biology and Neuroscience
More informationCOMPUTATIONAL INTELLIGENCE Vol. I - Associative Learning - Hava T. Siegelmann, Robert Kozma
ASSOCIATIVE LEARNING Hava T. Siegelmann University of Massachusetts Amherst, Amherst, MA, 01003, USA Robert Kozma Tennessee University Professor of Mathematics, The University of Memphis, USA Keywords:
More informationBrain & Behavior Syllabus V Instructor Mike Hawken Spring 2006
Brain & Behavior Syllabus V55.0306 Instructor Mike Hawken Spring 2006 Week 1 INTRODUCTION 1/17 Lecture 1 Introduction and History of Neuroscience early influences Reading: Chapter 1, pp 2 23 1/19 Lecture
More informationArtificial Neural Networks
Artificial Neural Networks Torsten Reil torsten.reil@zoo.ox.ac.uk Outline What are Neural Networks? Biological Neural Networks ANN The basics Feed forward net Training Example Voice recognition Applications
More informationSynfire chains with conductance-based neurons: internal timing and coordination with timed input
Neurocomputing 5 (5) 9 5 www.elsevier.com/locate/neucom Synfire chains with conductance-based neurons: internal timing and coordination with timed input Friedrich T. Sommer a,, Thomas Wennekers b a Redwood
More informationMemory: Computation, Genetics, Physiology, and Behavior. James L. McClelland Stanford University
Memory: Computation, Genetics, Physiology, and Behavior James L. McClelland Stanford University A Playwright s Take on Memory What interests me a great deal is the mistiness of the past Harold Pinter,
More informationLONG TERM MEMORY. Learning Objective Topics. Retrieval and the Brain. Retrieval Neuroscience of Memory. LTP Brain areas Consolidation Reconsolidation
LONG TERM MEMORY Retrieval and the rain Learning Objective Topics Retrieval Neuroscience of Memory LTP rain areas onsolidation Reconsolidation 1 Long-term memory How does info become encoded/stored in
More informationPrior Knowledge and Memory Consolidation Expanding Competitive Trace Theory
Prior Knowledge and Memory Consolidation Expanding Competitive Trace Theory Anna Smith Outline 1. 2. 3. 4. 5. Background in Memory Models Models of Consolidation The Hippocampus Competitive Trace Theory
More informationLearning in neural networks
http://ccnl.psy.unipd.it Learning in neural networks Marco Zorzi University of Padova M. Zorzi - European Diploma in Cognitive and Brain Sciences, Cognitive modeling", HWK 19-24/3/2006 1 Connectionist
More informationAcetylcholine again! - thought to be involved in learning and memory - thought to be involved dementia (Alzheimer's disease)
Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function Michael E. Hasselmo * and Bradley P. Wyble Acetylcholine again! - thought to
More informationCell Responses in V4 Sparse Distributed Representation
Part 4B: Real Neurons Functions of Layers Input layer 4 from sensation or other areas 3. Neocortical Dynamics Hidden layers 2 & 3 Output layers 5 & 6 to motor systems or other areas 1 2 Hierarchical Categorical
More informationEvaluating the Effect of Spiking Network Parameters on Polychronization
Evaluating the Effect of Spiking Network Parameters on Polychronization Panagiotis Ioannou, Matthew Casey and André Grüning Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK
More informationAn attractor network is a network of neurons with
Attractor networks Edmund T. Rolls An attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks
More informationPart 11: Mechanisms of Learning
Neurophysiology and Information: Theory of Brain Function Christopher Fiorillo BiS 527, Spring 2012 042 350 4326, fiorillo@kaist.ac.kr Part 11: Mechanisms of Learning Reading: Bear, Connors, and Paradiso,
More informationNeural coding and information theory: Grandmother cells v. distributed codes
Neural coding and information theory: Grandmother cells v. distributed codes John Collins 1/20 Strengths of computers v. brains Computer Brain Accurate and fast computation Accurate and fast storage Doesn
More information7.012 Problem Set 7. c) What % of females in this population should be red-green colorblind?
MIT Biology Department 7.012: Introductory Biology - Fall 2004 Instructors: Professor Eric Lander, Professor Robert A. Weinberg, Dr. Claudette Gardel Name: Question 1 7.012 Problem Set 7 Please print out
More informationBehavioral Neurobiology
Behavioral Neurobiology The Cellular Organization of Natural Behavior THOMAS J. CAREW University of California, Irvine Sinauer Associates, Inc. Publishers Sunderland, Massachusetts PART I: Introduction
More informationMemory retention the synaptic stability versus plasticity dilemma
Memory retention the synaptic stability versus plasticity dilemma Paper: Abraham, Wickliffe C., and Anthony Robins. "Memory retention the synaptic stability versus plasticity dilemma." Trends in neurosciences
More informationOxford Foundation for Theoretical Neuroscience and Artificial Intelligence
Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence For over two millennia, philosophers and scientists
More informationThis Lecture: Psychology of Memory and Brain Areas Involved
Lecture 18 (Nov 24 th ): LEARNING & MEMORY #1 Lecture Outline This Lecture: Psychology of Memory and Brain Areas Involved Next lecture: Neural Mechanisms for Memory 1) Psychology of Memory: Short Term
More informationComputational approach to the schizophrenia: disconnection syndrome and dynamical pharmacology
Computational approach to the schizophrenia: disconnection syndrome and dynamical pharmacology Péter Érdi1,2, Brad Flaugher 1, Trevor Jones 1, Balázs Ujfalussy 2, László Zalányi 2 and Vaibhav Diwadkar
More informationNeuroplasticity What Graduate Students Need to Know about Learning, Memory and Development
8/25/17 Neuroplasticity What Graduate Students Need to Know about Learning, Memory and Development Carrie B. Myers, Anna Zelaya, and Catherine M. Johnson Hebbian Theory What you focus on, you literally
More informationHolding Multiple Items in Short Term Memory: A Neural Mechanism
: A Neural Mechanism Edmund T. Rolls 1 *, Laura Dempere-Marco 1,2, Gustavo Deco 3,2 1 Oxford Centre for Computational Neuroscience, Oxford, United Kingdom, 2 Department of Information and Communication
More informationEncoding Spatial Context: A Hypothesis on the Function of the Dentate Gyrus-Hilus System
Encoding Spatial Context: A Hypothesis on the Function of the Dentate Gyrus-Hilus System A. Minai, ECECS Department, University of Cincinnati, Cincinnati, OH J. Best, Department of Psychology, Miami University,
More informationPSY 215 Lecture 13 (3/7/11) Learning & Memory Dr. Achtman PSY 215. Lecture 13 Topic: Mechanisms of Learning and Memory Chapter 13, section 13.
PSY 215 Lecture 13 Topic: Mechanisms of Learning and Memory Chapter 13, section 13.2 Corrections: No corrections Announcements: Question # 37 was thrown out on the last test because it was confusing the
More informationFree recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function
Behavioural Brain Research 89 (1997) 1 34 Review article Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function Michael E. Hasselmo
More informationPreparing More Effective Liquid State Machines Using Hebbian Learning
2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006 Preparing More Effective Liquid State Machines Using Hebbian Learning
More informationComputing with Spikes in Recurrent Neural Networks
Computing with Spikes in Recurrent Neural Networks Dezhe Jin Department of Physics The Pennsylvania State University Presented at ICS Seminar Course, Penn State Jan 9, 2006 Outline Introduction Neurons,
More informationStorage: Retaining Information
PSYCHOLOGY (8th Edition, in Modules) David Myers PowerPoint Slides Worth Publishers, 2007 1 Storage: Retaining Information Module 26 2 Storage: Retaining Information Storage: Retaining Information Sensory
More informationarxiv: v1 [cs.ne] 18 Mar 2017
A wake-sleep algorithm for recurrent, spiking neural networks arxiv:1703.06290v1 [cs.ne] 18 Mar 2017 Johannes C. Thiele Physics Master Program ETH Zurich johannes.thiele@alumni.ethz.ch Matthew Cook Institute
More informationNeuroplasticity:. Happens in at least 3 ways: - - -
BRAIN PLASTICITY Neuroplasticity:. Happens in at least 3 ways: - - - Recently, it was found that new neurons and glial cells are born in specific brain regions - reorganization. Brain plasticity occurs
More informationVS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker. Neuronenmodelle III. Modelle synaptischer Kurz- und Langzeitplastizität
Bachelor Program Bioinformatics, FU Berlin VS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker Synaptische Übertragung Neuronenmodelle III Modelle synaptischer Kurz- und Langzeitplastizität
More informationBRAIN PLASTICITY. Neuroplasticity:. Happens in at least 3 ways: - - -
BRAIN PLASTICITY Neuroplasticity:. Happens in at least 3 ways: - - - Recently, it was found that new neurons and glial cells are born in specific brain regions - reorganization. Brain plasticity occurs
More informationIntelligent Control Systems
Lecture Notes in 4 th Class in the Control and Systems Engineering Department University of Technology CCE-CN432 Edited By: Dr. Mohammed Y. Hassan, Ph. D. Fourth Year. CCE-CN432 Syllabus Theoretical: 2
More informationLECTURE 2. C. Reason correlation and synaptic delay not enough to prove direct connection. D. Underlying mechanism behind oscillations possibilities
LECTURE 2 A. Identifying Swimmy neurons B. Finding E and I inputs to cells 1 and 2 C. Reason correlation and synaptic delay not enough to prove direct connection D. Underlying mechanism behind oscillations
More informationFrom spike frequency to free recall: How neural circuits perform encoding and retrieval.
Hasselmo et al. 1 From spike frequency to free recall: How neural circuits perform encoding and retrieval. Michael Hasselmo, Bradley P. Wyble, Robert C. Cannon Department of Psychology and Program in Neuroscience
More informationRepresentational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex
Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex Yuichi Katori 1,2 *, Kazuhiro Sakamoto 3, Naohiro Saito 4, Jun Tanji 4, Hajime
More informationIntroduction to Physiological Psychology Learning and Memory II
Introduction to Physiological Psychology Learning and Memory II ksweeney@cogsci.ucsd.edu cogsci.ucsd.edu/~ksweeney/psy260.html Memory Working Memory Long-term Memory Declarative Memory Procedural Memory
More informationTheories of memory. Memory & brain Cellular bases of learning & memory. Epileptic patient Temporal lobectomy Amnesia
Cognitive Neuroscience: The Biology of the Mind, 2 nd Ed., M. S. Gazzaniga, R. B. Ivry, and G. R. Mangun, Norton, 2002. Theories of Sensory, short-term & long-term memories Memory & brain Cellular bases
More informationSynaptic plasticity. Mark van Rossum. Institute for Adaptive and Neural Computation University of Edinburgh
Synaptic plasticity Mark van Rossum Institute for Adaptive and Neural Computation University of Edinburgh 1 Human memory systems 2 Psychologists have split up memory in: Declarative memory * Episodic memory
More information35-2 The Nervous System Slide 1 of 38
1 of 38 35-2 The Nervous System The nervous system controls and coordinates functions throughout the body and responds to internal and external stimuli. 2 of 38 Neurons Neurons The messages carried by
More informationSUPPLEMENTARY INFORMATION
doi:10.1038/nature10776 Supplementary Information 1: Influence of inhibition among blns on STDP of KC-bLN synapses (simulations and schematics). Unconstrained STDP drives network activity to saturation
More informationObservational Learning Based on Models of Overlapping Pathways
Observational Learning Based on Models of Overlapping Pathways Emmanouil Hourdakis and Panos Trahanias Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH) Science and Technology
More informationCRISP: Challenging the Standard Framework of Hippocampal Memory Function
CRISP: Challenging the Standard Framework of Hippocampal Memory Function Laurenz Wiskott Mehdi Bayati Sen Cheng Jan Melchior Torsten Neher supported by: Deutsche Forschungsgemeinschaft - Sonderforschungsbereich
More informationWinter 2017 PHYS 178/278 Project Topics
Winter 2017 PHYS 178/278 Project Topics 1. Recurrent Neural Integrator Network Model for Horizontal Eye Position Stability of the Memory of Eye Position in a Recurrent Network of Conductance- Based Model
More informationA neural circuit model of decision making!
A neural circuit model of decision making! Xiao-Jing Wang! Department of Neurobiology & Kavli Institute for Neuroscience! Yale University School of Medicine! Three basic questions on decision computations!!
More informationCOGNITIVE SCIENCE 107A. Hippocampus. Jaime A. Pineda, Ph.D.
COGNITIVE SCIENCE 107A Hippocampus Jaime A. Pineda, Ph.D. Common (Distributed) Model of Memory Processes Time Course of Memory Processes Long Term Memory DECLARATIVE NON-DECLARATIVE Semantic Episodic Skills
More informationNeurobiology and Information Processing Theory: the science behind education
Educational Psychology Professor Moos 4 December, 2008 Neurobiology and Information Processing Theory: the science behind education If you were to ask a fifth grader why he goes to school everyday, he
More informationarxiv: v1 [q-bio.nc] 25 Apr 2017
Neurogenesis and multiple plasticity mechanisms enhance associative memory retrieval in a spiking network model of the hippocampus arxiv:1704.07526v1 [q-bio.nc] 25 Apr 2017 Yansong, Chua and Cheston, Tan
More informationA general error-based spike-timing dependent learning rule for the Neural Engineering Framework
A general error-based spike-timing dependent learning rule for the Neural Engineering Framework Trevor Bekolay Monday, May 17, 2010 Abstract Previous attempts at integrating spike-timing dependent plasticity
More informationMulti-Associative Memory in flif Cell Assemblies
Multi-Associative Memory in flif Cell Assemblies Christian R. Huyck (c.huyck@mdx.ac.uk) Kailash Nadh (k.nadh@mdx.ac.uk) School of Engineering and Information Sciences Middlesex University London, UK Abstract
More informationwill examine how the network behaves under exposure of different concentrations of drugs affecting the AMPA/kainate, NMDA, and, serotonin (5-HT) recep
2D1435, Mathematical Modeling of Biological Systems, 2002 Lab 4: Biological Neural Networks 1 Objectives In this exercise you will study how properties on the molecular and cellular levels can give rise
More informationElliot D. Menschik, Shih-Cheng Yen, and Leif H. Finkel
To appear in Computational Neuroscience: Trends in Research, 1998 (J.M. Bower, ed.) ATTRACTOR DYNAMICS IN REALISTIC HIPPOCAMPAL NETWORKS Elliot D. Menschik, Shih-Cheng Yen, and Leif H. Finkel Institute
More informationLong-term depression and recognition of parallel "bre patterns in a multi-compartmental model of a cerebellar Purkinje cell
Neurocomputing 38}40 (2001) 383}388 Long-term depression and recognition of parallel "bre patterns in a multi-compartmental model of a cerebellar Purkinje cell Volker Steuber*, Erik De Schutter Laboratory
More informationA General Theory of the Brain Based on the Biophysics of Prediction
A General Theory of the Brain Based on the Biophysics of Prediction Christopher D. Fiorillo KAIST Daejeon, Korea April 7, 2016 Why have we not understood the brain? Scientists categorize the world into
More informationLearning. Learning: Problems. Chapter 6: Learning
Chapter 6: Learning 1 Learning 1. In perception we studied that we are responsive to stimuli in the external world. Although some of these stimulus-response associations are innate many are learnt. 2.
More informationThe Ever-Changing Brain. Dr. Julie Haas Biological Sciences
The Ever-Changing Brain Dr. Julie Haas Biological Sciences Outline 1) Synapses: excitatory, inhibitory, and gap-junctional 2) Synaptic plasticity, and Hebb s postulate 3) Sensory maps and plasticity 4)
More informationSynaptic theory of gradient learning with empiric inputs. Ila Fiete Kavli Institute for Theoretical Physics. Types of learning
Synaptic theory of gradient learning with empiric inputs Ila Fiete Kavli Institute for Theoretical Physics Types of learning Associations among events (and reinforcements). e.g., classical conditioning
More informationAn attractor hypothesis of obsessive compulsive disorder
European Journal of Neuroscience European Journal of Neuroscience, pp. 1 13, 2008 doi:10.1111/j.1460-9568.2008.06379.x An attractor hypothesis of obsessive compulsive disorder Edmund T. Rolls, 1 Marco
More informationLecture 1: Neurons. Lecture 2: Coding with spikes. To gain a basic understanding of spike based neural codes
Lecture : Neurons Lecture 2: Coding with spikes Learning objectives: To gain a basic understanding of spike based neural codes McCulloch Pitts Neuron I w in Σ out Θ Examples: I = ; θ =.5; w=. - in = *.
More information11/2/2011. Basic circuit anatomy (the circuit is the same in all parts of the cerebellum)
11/2/2011 Neuroscientists have been attracted to the puzzle of the Cerebellum ever since Cajal. The orderly structure, the size of the cerebellum and the regularity of the neural elements demands explanation.
More informationThe Central Auditory System
THE AUDITORY SYSTEM Each auditory nerve sends information to the cochlear nucleus. The Central Auditory System From there, projections diverge to many different pathways. The Central Auditory System There
More informationMemory Systems II How Stored: Engram and LTP. Reading: BCP Chapter 25
Memory Systems II How Stored: Engram and LTP Reading: BCP Chapter 25 Memory Systems Learning is the acquisition of new knowledge or skills. Memory is the retention of learned information. Many different
More informationNature Neuroscience: doi: /nn Supplementary Figure 1. Behavioral training.
Supplementary Figure 1 Behavioral training. a, Mazes used for behavioral training. Asterisks indicate reward location. Only some example mazes are shown (for example, right choice and not left choice maze
More informationChapter 6: Hippocampal Function In Cognition. From Mechanisms of Memory, second edition By J. David Sweatt, Ph.D.
Chapter 6: Hippocampal Function In Cognition From Mechanisms of Memory, second edition By J. David Sweatt, Ph.D. Grid Cell The Hippocampus Serves a Role in Multimodal Information Processing and Memory
More informationAssociative memory properties of multiple cortical modules
Network: Comput. Neural Syst. 10 (1999) 237 255. Printed in the UK PII: S0954-898X(99)97275-5 Associative memory properties of multiple cortical modules Alfonso Renart, Néstor Parga and Edmund T Rolls
More informationRecurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning
Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning John V. Arthur and Kwabena Boahen Department of Bioengineering, University of Pennsylvania Philadelphia, PA 19104, U.S.A.
More informationReversed and forward buffering of behavioral spike sequences enables retrospective and prospective retrieval in hippocampal regions CA3 and CA1
Neural Networks 21 (2008) 276 288 www.elsevier.com/locate/neunet 2008 Special Issue Reversed and forward buffering of behavioral spike sequences enables retrospective and prospective retrieval in hippocampal
More informationRequired Text: Biological Psychology Breedlove et al. Sinauer, 2007, Fifth Edition
Brain and Behavior, V55.0306 Mike Hawken Spring 2010 This is a MAP course which satisfies the Natural Science II requirement. The lectures are scheduled for 2:00-3:15, Tuesdays and Thursdays, Room 207
More informationSUPPLEMENTARY INFORMATION. Supplementary Figure 1
SUPPLEMENTARY INFORMATION Supplementary Figure 1 The supralinear events evoked in CA3 pyramidal cells fulfill the criteria for NMDA spikes, exhibiting a threshold, sensitivity to NMDAR blockade, and all-or-none
More informationBundles of Synergy A Dynamical View of Mental Function
Bundles of Synergy A Dynamical View of Mental Function Ali A. Minai University of Cincinnati University of Cincinnati Laxmi Iyer Mithun Perdoor Vaidehi Venkatesan Collaborators Hofstra University Simona
More informationYou submitted this quiz on Sun 19 May :32 PM IST (UTC +0530). You got a score of out of
Feedback Ex6 You submitted this quiz on Sun 19 May 2013 9:32 PM IST (UTC +0530). You got a score of 10.00 out of 10.00. Question 1 What is common to Parkinson, Alzheimer and Autism? Electrical (deep brain)
More informationActivity 2 The Brain and Drugs
Activity 2 The Brain and Drugs Core Concept: Addictive drugs affect signaling at the synapses in the reward pathway of the brain. Class time required: Approximately 40-60 minutes Teacher Provides: For
More informationNEURONS COMMUNICATE WITH OTHER CELLS AT SYNAPSES 34.3
NEURONS COMMUNICATE WITH OTHER CELLS AT SYNAPSES 34.3 NEURONS COMMUNICATE WITH OTHER CELLS AT SYNAPSES Neurons communicate with other neurons or target cells at synapses. Chemical synapse: a very narrow
More informationCh 8. Learning and Memory
Ch 8. Learning and Memory Cognitive Neuroscience: The Biology of the Mind, 2 nd Ed., M. S. Gazzaniga, R. B. Ivry, and G. R. Mangun, Norton, 2002. Summarized by H.-S. Seok, K. Kim, and B.-T. Zhang Biointelligence
More informationStudy of the Brain. Notes
Study of the Brain Notes 1. Three Components of the Brain Cerebrum. Most high-level brain functions take place in the cerebrum. It is divided into the left and right hemispheres. Many motor and sensory
More informationCh 8. Learning and Memory
Ch 8. Learning and Memory Cognitive Neuroscience: The Biology of the Mind, 2 nd Ed., M. S. Gazzaniga,, R. B. Ivry,, and G. R. Mangun,, Norton, 2002. Summarized by H.-S. Seok, K. Kim, and B.-T. Zhang Biointelligence
More informationActive Sites model for the B-Matrix Approach
Active Sites model for the B-Matrix Approach Krishna Chaithanya Lingashetty Abstract : This paper continues on the work of the B-Matrix approach in hebbian learning proposed by Dr. Kak. It reports the
More informationDecision-making mechanisms in the brain
Decision-making mechanisms in the brain Gustavo Deco* and Edmund T. Rolls^ *Institucio Catalana de Recerca i Estudis Avangats (ICREA) Universitat Pompeu Fabra Passeigde Circumval.lacio, 8 08003 Barcelona,
More informationSynchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses
Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses Adria Bofill-i-Petit The University of Edinburgh Edinburgh, EH9 JL Scotland adria.bofill@ee.ed.ac.uk Alan F. Murray The University
More informationNoise in attractor networks in the brain produced by graded firing rate representations
Noise in attractor networks in the brain produced by graded firing rate representations Tristan J. Webb, University of Warwick, Complexity Science, Coventry CV4 7AL, UK Edmund T. Rolls, Oxford Centre for
More information