What is a chunk? Barbara Oakley, PhD, PE
|
|
|
- John Smart
- 6 years ago
- Views:
Transcription
1 What is a chunk? Barbara Oakley, PhD, PE
2
3
4
5 (write the letters here that appear on the screen.)
6
7
8
9 Relevant Readings Beilock, S. (2010). Choke. NY: Free Press. Ericsson, K. A. (2009). Development of Professional Expertise. NY: Cambridge University Press. Gobet, F., & Clarkson, G. (2004). Chunks in expert memory: Evidence for the magical number four or is it two? Memory, 12(6), Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I., & Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5(6), Guida, A., Gobet, F., Tardieu, H., & Nicolas, S. (2012). How chunks, long-term working memory and templates offer a cognitive explanation for neuroimaging data on expertise acquisition: A two-stage framework. Brain and Cognition, 79(3), doi: /j.bandc Nyhus, E., & Curran, T. (2010). Functional role of gamma and theta oscillations in episodic memory. Neuroscience and Biobehavioral Reviews, 34(7), doi: /j.neubiorev Image Credits Neurons image 4 slots of working memory and cerebral cortex Kevin Mendez and Barbara Oakley, 2014 Defense Language Institute logo, Octopus of attention and diffuse tentacles, images Kevin Mendez, 2014 Pinball images Kevin Mendez and Barbara Oakley, 2014 Puzzle images Kevin Mendez and Philip Oakley, 2014 Neurons firing together Kevin Mendez, 2014 Clip art courtesy Microsoft Corporation Music Credits Ashley Hall free music
Temporal Neuronal Oscillations can Produce Spatial Phase Codes
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/237845243 Temporal Neuronal Oscillations can Produce Spatial Phase Codes Chapter in Attention
How to make computers work like the brain
How to make computers work like the brain (without really solving the brain) Dileep George a single special machine can be made to do the work of all. It could in fact be made to work as a model of any
Fundamentals of Computational Neuroscience 2e
Fundamentals of Computational Neuroscience 2e Thomas Trappenberg March 21, 2009 Chapter 9: Modular networks, motor control, and reinforcement learning Mixture of experts Expert 1 Input Expert 2 Integration
Human Brain Networks. Aivoaakkoset BECS-C3001"
Human Brain Networks Aivoaakkoset BECS-C3001" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" www.glerean.com @eglerean becs.aalto.fi/bml enrico.glerean@aalto.fi" Why?" 1. WHY BRAIN NETWORKS?"
Helmholtz Machines Memory Fluctuation Dreams
Helmholtz Machines Memory Fluctuation Dreams Academy of Media Arts, Cologne June 28, 2006 Kevin G. Kirby Evan and Lindsay Stein Professor of Biocomputing Department of Computer Science, Northern Kentucky
Songting Li. Applied Mathematics, Mathematical and Computational Neuroscience, Biophysics
Songting Li Contact Information Phone: +1-917-930-3505 email: songting@cims.nyu.edu homepage: http://www.cims.nyu.edu/ songting/ Address: Courant Institute, 251 Mercer Street, New York, NY, United States,
Discovery Channel. Biped
Discovery Channel Biped Schaal S, Sternad D, Osu R, Kawato M: Rhythmic arm movement is not discrete Nature Neuroscience, 7, 11371144 (004) Nakanishi J, Morimoto J, Endo G, Cheng G, Schaal S, Kawato M:
Basic MRI physics and Functional MRI
Basic MRI physics and Functional MRI Gregory R. Lee, Ph.D Assistant Professor, Department of Radiology June 24, 2013 Pediatric Neuroimaging Research Consortium Objectives Neuroimaging Overview MR Physics
In the Name of God. Lecture 9: ANN Architectures
In the Name of God Lecture 9: ANN Architectures Biological Neuron Organization of Levels in Brains Central Nervous sys Interregional circuits Local circuits Neurons Dendrite tree map into cerebral cortex,
Experimental design of fmri studies
Experimental design of fmri studies Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian Ruff SPM Course 2015 Overview of SPM Image time-series Kernel
6th ICA Mountain Cartography Workshop Lenk/Switzerland. Cartographic Design Issues Utilizing Google Earth for Spatial Communication
6th ICA Mountain Cartography Workshop Lenk/Switzerland Cartographic Design Issues Utilizing Google Earth for Spatial Communication Department of Geography and Regional Research Karel Kriz University Vienna,
Zoomer s Event Kit. Illustrations Ned Young.
Zoomer s Event Kit Catch him if you can! Illustrations Ned Young MEET ZOOMER IF YOU CAN CATCH HIM! Zoomer is a very busy puppy. He has to blow the world s biggest bubble, build a brontosaurus out of sand,
Understanding Aha! Moments
Understanding Aha! Moments Hermish Mehta Department of Electrical Engineering & Computer Sciences University of California, Berkeley Berkeley, CA 94720 hermish@berkeley.edu Abstract In this paper, we explore
Computational Neuroscience Introduction Day
Computational Neuroscience Introduction Day 9.30am Introduction (C.Machens) 10am M1 (C.Machens) 10.15am M2 (V. Hakim) 10.40 break 11.00 Matching Law (S. Deneve) 14.00 Student presentations 11.20 Rescorla-Wagner
Lecture 4: Importance of Noise and Fluctuations
Lecture 4: Importance of Noise and Fluctuations Jordi Soriano Fradera Dept. Física de la Matèria Condensada, Universitat de Barcelona UB Institute of Complex Systems September 2016 1. Noise in biological
Experimental design of fmri studies
Experimental design of fmri studies Zurich SPM Course 2016 Sandra Iglesias Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT) University and ETH Zürich With many thanks for
Searching for Nested Oscillations in Frequency and Sensor Space. Will Penny. Wellcome Trust Centre for Neuroimaging. University College London.
in Frequency and Sensor Space Oscillation Wellcome Trust Centre for Neuroimaging. University College London. Non- Workshop on Non-Invasive Imaging of Nonlinear Interactions. 20th Annual Computational Neuroscience
Address for Correspondence
Research Article APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR INTERFERENCE STUDIES OF LOW-RISE BUILDINGS 1 Narayan K*, 2 Gairola A Address for Correspondence 1 Associate Professor, Department of Civil
Hierarchy. Will Penny. 24th March Hierarchy. Will Penny. Linear Models. Convergence. Nonlinear Models. References
24th March 2011 Update Hierarchical Model Rao and Ballard (1999) presented a hierarchical model of visual cortex to show how classical and extra-classical Receptive Field (RF) effects could be explained
Experimental design of fmri studies & Resting-State fmri
Methods & Models for fmri Analysis 2016 Experimental design of fmri studies & Resting-State fmri Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian
Strength-based and time-based supervised networks: Theory and comparisons. Denis Cousineau Vision, Integration, Cognition lab. Université de Montréal
Strength-based and time-based supervised networks: Theory and comparisons Denis Cousineau Vision, Integration, Cognition lab. Université de Montréal available at: http://mapageweb.umontreal.ca/cousined
Artificial Neural Network
Artificial Neural Network Contents 2 What is ANN? Biological Neuron Structure of Neuron Types of Neuron Models of Neuron Analogy with human NN Perceptron OCR Multilayer Neural Network Back propagation
Decoding Cognitive Processes from Functional MRI
Decoding Cognitive Processes from Functional MRI Oluwasanmi Koyejo 1 and Russell A. Poldrack 2 1 Imaging Research Center, University of Texas at Austin sanmi.k@utexas.edu 2 Depts. of Psychology and Neuroscience,
IEICE TRANS. INF. & SYST., VOL.E96 D, NO.6 JUNE
1387 PAPER An Explanation of Signal Changes in DW-fMRI: Monte Carlo Simulation Study of Restricted Diffusion of Water Molecules Using 3D and Two-Compartment Cortical Cell Models Shizue NAGAHARA, a), Student
Motion Perception 1. PSY305 Lecture 12 JV Stone
Motion Perception 1 PSY305 Lecture 12 JV Stone 1 Structure Human visual system as a band-pass filter. Neuronal motion detection, the Reichardt detector. The aperture problem. 2 The visual system is a temporal
A Cell Assembly Model of Sequential Memory
A Cell Assembly Model of Sequential Memory Hina Ghalib, Christian Huyck Abstract Perception, prediction and generation of sequences is a fundamental aspect of human behavior and depends on the ability
Decoding conceptual representations
Decoding conceptual representations!!!! Marcel van Gerven! Computational Cognitive Neuroscience Lab (www.ccnlab.net) Artificial Intelligence Department Donders Centre for Cognition Donders Institute for
Modelling stochastic neural learning
Modelling stochastic neural learning Computational Neuroscience András Telcs telcs.andras@wigner.mta.hu www.cs.bme.hu/~telcs http://pattern.wigner.mta.hu/participants/andras-telcs Compiled from lectures
Influence of Criticality on 1/f α Spectral Characteristics of Cortical Neuron Populations
Influence of Criticality on 1/f α Spectral Characteristics of Cortical Neuron Populations Robert Kozma rkozma@memphis.edu Computational Neurodynamics Laboratory, Department of Computer Science 373 Dunn
Cognitive Prosem. December 1, 2008
Cognitive Prosem December 1, 2008 Spatial Representation What does it mean to represent spatial information? What are the characteristics of human spatial representations? Reference Frames/Systems Distortions
MULTISCALE MODULARITY IN BRAIN SYSTEMS
MULTISCALE MODULARITY IN BRAIN SYSTEMS Danielle S. Bassett University of California Santa Barbara Department of Physics The Brain: A Multiscale System Spatial Hierarchy: Temporal-Spatial Hierarchy: http://www.idac.tohoku.ac.jp/en/frontiers/column_070327/figi-i.gif
Synchrony in Neural Systems: a very brief, biased, basic view
Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011 components of neuronal networks neurons synapses connectivity cell type - intrinsic
The Bayesian Brain. Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester. May 11, 2017
The Bayesian Brain Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester May 11, 2017 Bayesian Brain How do neurons represent the states of the world? How do neurons represent
Again it does not matter if the number is negative or positive. On a number line, 6 is 6 units to the right of 0. So the absolute value is 6.
Name Working with Absolute Value - Step-by-Step Lesson Lesson 1 Absolute Value Problem: 1. Find the absolute value. 6 = Explanation: Absolute values focus on the size or magnitude of a number. They do
Synfire Waves in Small Balanced Networks
Synfire Waves in Small Balanced Networks Yuval Aviel 1,3, David Horn 2 and Moshe Abeles 1 1 Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. 2 School of Physics and
Neuronal Dynamics: Computational Neuroscience of Single Neurons
Week 4 part 5: Nonlinear Integrate-and-Fire Model 4.1 From Hodgkin-Huxley to 2D Neuronal Dynamics: Computational Neuroscience of Single Neurons Week 4 Recing detail: Two-dimensional neuron models Wulfram
We will begin momentarily at 2pm ET. Slides available now! Recordings are available to ACS members.
We will begin momentarily at 2pm ET Slides available now! Recordings are available to ACS members. www.acs.org/acswebinars Contact ACS Webinars at acswebinars@acs.org 1 Have Questions? Why am I muted?
Artificial Neural Network and Fuzzy Logic
Artificial Neural Network and Fuzzy Logic 1 Syllabus 2 Syllabus 3 Books 1. Artificial Neural Networks by B. Yagnanarayan, PHI - (Cover Topologies part of unit 1 and All part of Unit 2) 2. Neural Networks
Brains and Computation
15-883: Computational Models of Neural Systems Lecture 1.1: Brains and Computation David S. Touretzky Computer Science Department Carnegie Mellon University 1 Models of the Nervous System Hydraulic network
Philosophy of Science: Models in Science
Philosophy of Science: Models in Science Kristina Rolin 2012 Questions What is a scientific theory and how does it relate to the world? What is a model? How do models differ from theories and how do they
COMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, Hanna Kurniawati
COMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, 3.1-3.3 Hanna Kurniawati Today } What is Artificial Intelligence? } Better know what it is first before committing the
Multilayer and dynamic networks
OHBM 2016 Multilayer and dynamic networks Danielle S. Bassett University of Pennsylvania Department of Bioengineering Outline Statement of the problem Statement of a solution: Multilayer Modeling Utility
Stability of Amygdala Learning System Using Cell-To-Cell Mapping Algorithm
Stability of Amygdala Learning System Using Cell-To-Cell Mapping Algorithm Danial Shahmirzadi, Reza Langari Department of Mechanical Engineering Texas A&M University College Station, TX 77843 USA danial_shahmirzadi@yahoo.com,
Response Selection Using Neural Phase Oscillators
Response Selection Using Neural Phase Oscillators J. Acacio de Barros 1 A symposium on the occasion of Patrick Suppes 90th birthday March 10, 2012 1 With Pat Suppes and Gary Oas (Suppes et al., 2012).
Variational iteration method for q-difference equations of second order
Sichuan University From the SelectedWorks of G.C. Wu Summer June 6, 1 Variational iteration method for -difference euations of second order Guo-Cheng Wu Available at: https://works.bepress.com/gcwu/1/
The homogeneous Poisson process
The homogeneous Poisson process during very short time interval Δt there is a fixed probability of an event (spike) occurring independent of what happened previously if r is the rate of the Poisson process,
A spike sorting framework using nonparametric detection and incremental clustering
Neurocomputing 69 (26) 138 1384 www.elsevier.com/locate/neucom A spike sorting framework using nonparametric detection and incremental clustering Mingzhou (Joe) Song a,, Hongbin Wang b a Department of
Bridging the Practices of Two Communities
Educational Knowledge Domain Visualizations: Tools to Navigate, Understand, and Internalize the Structure of Scholarly Knowledge and Expertise Peter A. Hook http://ella.slis.indiana.edu/~pahook Mapping
Visual motion processing and perceptual decision making
Visual motion processing and perceptual decision making Aziz Hurzook (ahurzook@uwaterloo.ca) Oliver Trujillo (otrujill@uwaterloo.ca) Chris Eliasmith (celiasmith@uwaterloo.ca) Centre for Theoretical Neuroscience,
Guide to the Science Area For History and Science Concentrators Science and Society Track
Guide to the Science Area For History and Science Concentrators Science and Society Track 2017-2018 Department of the History of Science This guide provides ideas for designing your own program of study
Roman V. Belavkin School of Computing Science, Middlesex University, London NW4 4BT, UK
In F. Detje, D. Dörner, & H. Schaub (Eds.), Proceedings of the Fifth International Conference on Cognitive ling (pp. ). Bamberg, Germany: Universitäts Verlag Bamberg,. The Use of Entropy for Analysis and
ability to analyze and interpret data: Making connections to the scientific practices
ability to analyze and interpret data: Making connections to the scientific practices Claudia Lutz, Rob Wallon, Claire Scavuzzo, Sara Patterson Adamek, Barbara Hug University of Illinois Or: Nature and
Localized Excitations in Networks of Spiking Neurons
Localized Excitations in Networks of Spiking Neurons Hecke Schrobsdorff Bernstein Center for Computational Neuroscience Göttingen Max Planck Institute for Dynamics and Self-Organization Seminar: Irreversible
Experimental design of fmri studies
Experimental design of fmri studies Sandra Iglesias Translational Neuromodeling Unit University of Zurich & ETH Zurich With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian
Memories Associated with Single Neurons and Proximity Matrices
Memories Associated with Single Neurons and Proximity Matrices Subhash Kak Oklahoma State University, Stillwater Abstract: This paper extends the treatment of single-neuron memories obtained by the use
Multimedia Interactive Simulations of Physical Experiments
Multimedia Interactive Simulations of Physical Experiments Alena KOVÁROVÁ Department of Computer Graphics and Image Processing Faculty of Mathematics, Physics and Informatics, Comenius University Mlynská
NERVOUS OSCILLATIONS ON A TWISTED CYLINDER
NERVOUS OSCILLATIONS ON A TWISTED CYLINDER Arturo Tozzi Center for Nonlinear Science, University of North Texas 1155 Union Circle, #311427 Denton, TX 76203-5017 USA E-mail address: tozziarturo@libero.it
We all live in a yellow submarine
THE ART OF QUANTUM We all live in We all live in a yellow submarine We all live in quantum Universe Classicality is an emergent phenomenon everything is rooted in the realm of quantum (Classical) Reality
Computational Modeling for Physical Sciences
Computational Modeling for Physical Sciences Since the invention of computers the use of computational modeling and simulations have revolutionized the way we study physical systems. Their applications
A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain
A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain Dr. Ali Yener Mutlu Department of Electrical and Electronics Engineering, Izmir Katip Celebi
Defense Acquisition University
STEP 1 STEP 2 STEP 3 STEP 4 STEP 5 STEP 6 Search For Training Take DAU Orientation Take Online Training Take Instructor-Led Training Edit Your User Record View Your Course Completions Defense Acquisition
Math in systems neuroscience. Quan Wen
Math in systems neuroscience Quan Wen Human brain is perhaps the most complex subject in the universe 1 kg brain 10 11 neurons 180,000 km nerve fiber 10 15 synapses 10 18 synaptic proteins Multiscale
Quantum Probability in Cognition. Ryan Weiss 11/28/2018
Quantum Probability in Cognition Ryan Weiss 11/28/2018 Overview Introduction Classical vs Quantum Probability Brain Information Processing Decision Making Conclusion Introduction Quantum probability in
Cortical diffusion imaging
Cortical diffusion imaging Alard Roebroeck Maastricht Brain Imaging Center (MBIC) Dept. of Cognitive Neuroscience Faculty of Psychology & Neuroscience Maastricht University Diffusion MRI In vivo & Ex vivo
Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12
Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12 André Grüning, Brian Gardner and Ioana Sporea Department of Computer Science University of Surrey Guildford,
Biological Modeling of Neural Networks:
Week 14 Dynamics and Plasticity 14.1 Reservoir computing - Review:Random Networks - Computing with rich dynamics Biological Modeling of Neural Networks: 14.2 Random Networks - stationary state - chaos
Model of a Biological Neuron as a Temporal Neural Network
Model of a Biological Neuron as a Temporal Neural Network Sean D. Murphy and Edward W. Kairiss Interdepartmental Neuroscience Program, Department of Psychology, and The Center for Theoretical and Applied
The Art ofinformation Exchange in Our Body and in the Universe
Folie 1 The Art ofinformation Exchange in Our Body and in the Universe Dr. Rainer Viehweger manavibes.com Folie 2 Folie 3 Folie 4 Folie 5 Folie 6 Any difference? Folie 7 10 x 10 = 100 10 x 10 = 1010 134,53
Please read the following excerpt from an editorial about the Atkins diet
Please read the following excerpt from an editorial about the Atkins diet People are constantly looking for the best way to shed a few pounds. I think I found one. The Atkins diet is an effective diet.
Mathematical Magic. Abstract
Mathematical Magic BRIDGES Mathematical Connections in Art, Music, and Science Arthur T. Benjamin Department of Mathematics Harvey Mudd College 1250 N. Dartmouth Ave. Claremont, CA 91711 E-mail: benjamin
Challenging Students in STEM Using the Integrated Curriculum Model to teach STEM concepts
Challenging Students in STEM Using the Integrated Curriculum Model to teach STEM concepts Stephanie Zeiger, Ph.D. Engineer and Scientist February 28, 2014 The Integrated Curriculum Model and STEM CONTENT
Collecting Aligned Activity & Connectomic Data Example: Mouse Vibrissal Touch Barrel Cortex Exploiting Coherence to Reduce Dimensionality Example: C.
Collecting Aligned Activity & Connectomic Data Example: Mouse Vibrissal Touch Barrel Cortex Exploiting Coherence to Reduce Dimensionality Example: C. elegans Motor Control Sequence Spatially & Temporally
Sean Escola. Center for Theoretical Neuroscience
Employing hidden Markov models of neural spike-trains toward the improved estimation of linear receptive fields and the decoding of multiple firing regimes Sean Escola Center for Theoretical Neuroscience
Why Computational/Applied Mathematics?
January 5, 2008 The Origins of Calculus Who are the two people credited with the origination of calculus? Hint: Gravity Newton and Leibniz! Why? Algebra wasn t enough! Can algebra describe: 1. Planetary
Network Modeling and Functional Data Methods for Brain Functional Connectivity Studies. Kehui Chen
Network Modeling and Functional Data Methods for Brain Functional Connectivity Studies Kehui Chen Department of Statistics, University of Pittsburgh Nonparametric Statistics Workshop, Ann Arbor, Oct 06,
Fundamentals of Computational Neuroscience 2e
Fundamentals of Computational Neuroscience 2e January 1, 2010 Chapter 10: The cognitive brain Hierarchical maps and attentive vision A. Ventral visual pathway B. Layered cortical maps Receptive field size
The Genetics of Talent Development. Putting the Gift Back into Giftedness
The Genetics of Talent Development Putting the Gift Back into Giftedness Introduction: The Nature-Nurture Controversy Nature: Galton s (1869) Hereditary Genius Galton s (1874) English Men of Science Nurture:
Neural Networks Introduction
Neural Networks Introduction H.A Talebi Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Winter 2011 H. A. Talebi, Farzaneh Abdollahi Neural Networks 1/22 Biological
Supervisor: Prof. Stefano Spaccapietra Dr. Fabio Porto Student: Yuanjian Wang Zufferey. EPFL - Computer Science - LBD 1
Supervisor: Prof. Stefano Spaccapietra Dr. Fabio Porto Student: Yuanjian Wang Zufferey EPFL - Computer Science - LBD 1 Introduction Related Work Proposed Solution Implementation Important Results Conclusion
Experimental design of fmri studies
Methods & Models for fmri Analysis 2017 Experimental design of fmri studies Sara Tomiello With many thanks for slides & images to: Sandra Iglesias, Klaas Enno Stephan, FIL Methods group, Christian Ruff
Teacher s Pet Publications
Teacher s Pet Publications a unique educational resource company since 1989 To: Professional Language Arts Teachers From: Dr. James Scott, Teacher s Pet Publications Subject: Teacher s Pet Puzzle Packs
Spectral Interdependency Methods
Spectral Interdependency Methods Mukesh Dhamala* Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA Synonyms Coherence and Granger causality spectral
MEMORANDUM. Office of the Provost and Vice-Principal (Academic) Georgina Moore, Secretary of Senate. To: Susan P.C. Cole, Deputy Provost.
Page 14 MEMORANDUM To: From: Georgina Moore, Secretary of Senate Susan P.C. Cole, Deputy Provost Office of the Provost and Vice-Principal (Academic) Date: January 10, 2012 Subject: Schedule of Cyclical
Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies
Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies Andrea d Avella and Matthew C. Tresch Department of Brain and Cognitive Sciences Massachusetts
Artificial Neural Networks. Historical description
Artificial Neural Networks Historical description Victor G. Lopez 1 / 23 Artificial Neural Networks (ANN) An artificial neural network is a computational model that attempts to emulate the functions of
Using Variable Threshold to Increase Capacity in a Feedback Neural Network
Using Variable Threshold to Increase Capacity in a Feedback Neural Network Praveen Kuruvada Abstract: The article presents new results on the use of variable thresholds to increase the capacity of a feedback
Storage capacity of hierarchically coupled associative memories
Storage capacity of hierarchically coupled associative memories Rogério M. Gomes CEFET/MG - LSI Av. Amazonas, 7675 Belo Horizonte, MG, Brasil rogerio@lsi.cefetmg.br Antônio P. Braga PPGEE-UFMG - LITC Av.
Simultaneous Linear Equations 8. EE.C.8a, 8.EE.C.8c Conceptual Understanding and Application Mini-Assessment by Student Achievement Partners
Simultaneous Linear Equations 8. EE.C.8a, 8.EE.C.8c Conceptual Understanding and Application Mini-Assessment by Student Achievement Partners OVERVIEW This mini-assessment is designed to illustrate the
COGNITIVE INFORMATION DYNAMICS:
INC talk COGNITIVE INFORMATION DYNAMICS: INFORMATION FLOWS INSTABILITIES AND COORDINATION Life is like giving a concert on the violin while learning to play the instrument. That, friends, is real wisdom.
Lecture 4: Feed Forward Neural Networks
Lecture 4: Feed Forward Neural Networks Dr. Roman V Belavkin Middlesex University BIS4435 Biological neurons and the brain A Model of A Single Neuron Neurons as data-driven models Neural Networks Training
Illinois State University Department of Chemistry CHE Advanced Chemistry Demonstrations: Atomic and Molecular Structure.
Illinois State University Department of Chemistry CHE 401.04 Advanced Chemistry Demonstrations: Atomic and Molecular Structure. 3 credit hours Catalog Description: Advanced Chemistry Demonstrations: Atomic
A Biologically-Inspired Model for Recognition of Overlapped Patterns
A Biologically-Inspired Model for Recognition of Overlapped Patterns Mohammad Saifullah Department of Computer and Information Science Linkoping University, Sweden Mohammad.saifullah@liu.se Abstract. In
Sampling-based probabilistic inference through neural and synaptic dynamics
Sampling-based probabilistic inference through neural and synaptic dynamics Wolfgang Maass for Robert Legenstein Institute for Theoretical Computer Science Graz University of Technology, Austria Institute
You try: Protons. Electrons. Neutrons + + Name: Date: Period:
Atoms & Elements Name: Date: Period: Atoms are the basic units of. They have 3 parts -,, &. Atoms are because they have the same number of () and (-). - - - Protons charged particles that sit in the. The
Bayesian Evidence and Model Selection: A Tutorial in Two Acts
Bayesian Evidence and Model Selection: A Tutorial in Two Acts Kevin H. Knuth Depts. of Physics and Informatics, University at Albany, Albany NY USA Based on the paper: Knuth K.H., Habeck M., Malakar N.K.,
Natural Computation. Transforming Tools of NBIC: Complex Adaptive Systems. James P. Crutchfield Santa Fe Institute
Natural Computation Transforming Tools of NBIC: Complex Adaptive Systems James P. Crutchfield www.santafe.edu/~chaos Santa Fe Institute Nanotechnology, Biotechnology, Information Technology, and Cognitive
Consider the following spike trains from two different neurons N1 and N2:
About synchrony and oscillations So far, our discussions have assumed that we are either observing a single neuron at a, or that neurons fire independent of each other. This assumption may be correct in
The Physics in Psychology. Jonathan Flynn
The Physics in Psychology Jonathan Flynn Wilhelm Wundt August 16, 1832 - August 31, 1920 Freud & Jung 6 May 1856 23 September 26 July 1875 6 June Behaviorism September 14, 1849 February 27, 1936 August
Songting Li ( 李松挺 ) Applied Mathematics, Theoretical and Computational Neuroscience, Biophysics
Contact Information email: homepage: Address: Songting Li ( 李松挺 ) songting@sjtu.edu.cn Research Interests http://ins.sjtu.edu.cn/people/songtingli RM 363, Institute of Natural Sciences, Shanghai Jiao Tong
Computational Models of Human Cognition
Computational Models of Human Cognition Models A model is a means of representing the structure or workings of a system or object. e.g., model car e.g., economic model e.g., psychophysics model 2500 loudness