Introduction (2/2) in Adaptive Cooperative Systems by Martine Beckerman, 1997 09’ 7.10 B.-W. Ku.

Slides:

Advertisements

Similar presentations

Introduction to Neural Networks 2. Overview  The McCulloch-Pitts neuron  Pattern space  Limitations  Learning.

Advertisements

Neural Network Models in Vision Peter Andras

Lecture 13: Associative Memory References: D Amit, N Brunel, Cerebral Cortex 7, (1997) N Brunel, Network 11, (2000) N Brunel, Cerebral.

A new approach to Artificial Intelligence.  There are HUGE differences between brain architecture and computer architecture  The difficulty to emulate.

Image Segmentation by Complex-Valued Units Cornelius Weber and Stefan Wermter Hybrid Intelligent Systems School of Computing and Technology University.

Neuromodulation - signal-to-noise - switching - burst/single spike - oscillations.

A synaptic memory trace for cortical receptive field plasticity Robert Froemke, Michael Merzenich & Christoph Schreiner Andrew Lysaght HST.723 April 22,

Un Supervised Learning & Self Organizing Maps Learning From Examples

Synapses are everywhere neurons synapses Synapse change continuously –From msec –To hours (memory) Lack HH type model for the synapse.

The Perceptron. 0T Afferents V thr V rest t max 0 What does a neuron do? spike no spike.

Module : Development of the Nervous System Lecture 6 Synapse formation & refinement.

Michael P. Kilgard Sensory Experience and Cortical Plasticity University of Texas at Dallas.

The search for organizing principles of brain function Needed at multiple levels: synapse => cell => brain area (cortical maps) => hierarchy of areas.

Neural Networks Architecture Baktash Babadi IPM, SCS Fall 2004.

Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.

How Parkinson’s disease affects cortical information flow: A multiscale model Cliff Kerr Complex Systems Group University of Sydney Neurosimulation Laboratory.

Learning sensorimotor transformations Maurice J. Chacron.

Biological Modeling of Neural Networks Week 6 Hebbian LEARNING and ASSOCIATIVE MEMORY Wulfram Gerstner EPFL, Lausanne, Switzerland 6.1 Synaptic Plasticity.

Unit 4 Psychology Learning: Neural Pathways, Synapse Formation & the Role of Neurotransmitters.

Sensory Physiology. Concepts To Understand Receptor Potential Amplitude Coding Frequency Coding Activation/Inactivation Neural Adaptation Synaptic Depression.

Brain Rhythms: key questions  Coordination of processing across space  Coherence in perception, e.g. synchrony & binding, cognitive moment, causation/objects.

”When spikes do matter: speed and plasticity” Thomas Trappenberg 1.Generation of spikes 2.Hodgkin-Huxley equation 3.Beyond HH (Wilson model) 4.Compartmental.

Binding problems and feature integration theory. Feature detectors Neurons that fire to specific features of a stimulus Pathway away from retina shows.

Synaptic Dynamics: Unsupervised Learning

Neural Networks Presented by M. Abbasi Course lecturer: Dr.Tohidkhah.

Adaptive Cooperative Systems Chapter 8 Synaptic Plasticity 8.11 ~ 8.13 Summary by Byoung-Hee Kim Biointelligence Lab School of Computer Sci. & Eng. Seoul.

Image Segmentation by Complex-Valued Units Cornelius Weber Hybrid Intelligent Systems School of Computing and Technology University of Sunderland Presented.

Mean Field Theories in Neuroscience B. Cessac, Neuromathcomp, INRIA.

Ch 9. Rhythms and Synchrony 9.7 Adaptive Cooperative Systems, Martin Beckerman, Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National.

From LIF to HH Equivalent circuit for passive membrane The Hodgkin-Huxley model for active membrane Analysis of excitability and refractoriness using the.

Central Nervous System + Neural Networks

Biological Modeling of Neural Networks: Week 10 – Neuronal Populations Wulfram Gerstner EPFL, Lausanne, Switzerland 10.1 Cortical Populations - columns.

CHS AP Psychology Unit 3: Biological Psychology Essential Task 3-1: Identify the basic parts of the neuron (dendrites, cell body, axon, terminal buttons,

Synaptic Plasticity Synaptic efficacy (strength) is changing with time. Many of these changes are activity-dependent, i.e. the magnitude and direction.

Ch 8. Synaptic Plasticity 8.9 ~ 8.10 Adaptive Cooperative Systems, Martin Beckerman, Summarized by Kim, S. –J. Biointelligence Laboratory, Seoul.

Intro Author: Martin Beckerman Senior Scientist at the Department of Energy/National Nuclear Security Administration’s Y-12 National Security Complex in.

$200 $400 $600 $800 $1000 $200 $400 $600 $800 $1000 $200 $400 $600 $800 $1000 $200 $400 $600 $800 $1000 $200 $400 $600 $800 $1000 $200.

The Neural Code Baktash Babadi SCS, IPM Fall 2004.

The Neuron Who are the players?

Brain development.

Biointelligence Laboratory, Seoul National University

Richard Dorrance Literature Review: 1/11/13

Section 2 Interaction between neurons

Neuron Function.

Neurons and Nervous System

ورشة بعنوان استراتيجيات تعديل السلوك بين النظرية والتطبيق

Biological Bases of Behavior

Sensation: Some Neural Principles

Presented by Rhee, Je-Keun

Developmental neuroplasticity

أنماط الإدارة المدرسية وتفويض السلطة الدكتور أشرف الصايغ

Presented by Rhee, Je-Keun

Intelligent Leaning -- A Brief Introduction to Artificial Neural Networks Chiung-Yao Fang.

Computational neuroscience

Emre O. Neftci iScience Volume 5, Pages (July 2018) DOI: /j.isci

OCNC Statistical Approach to Neural Learning and Population Coding ---- Introduction to Mathematical.

Information Processing in Motor Learning

Presentation of Article:

Label Name Label Name Label Name Label Name Label Name Label Name

Learning Objective To learn the biological content of dream analysis. To understand what a neuron consists of and label a diagram.

Synchrony & Perception

The BIG question: how does a bunch of cells give rise to thinking?

The BIG question: how does a bunch of cells give rise to thinking?

Ch. 1. How could populations of neurons encode information

Ch. 5 The Patterning of Neural Connections 5. 1 ~ 5

Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories Marco Bocchio, Sadegh Nabavi,

Neural Network Models in Vision

Ch. 8 Synaptic Plasticity 8. 5 ~ 8

Spike timing-dependent plasticity

Fig. 1 Schematic of biological neuronal network and an ONW ST that emulates a biological synapse. Schematic of biological neuronal network and an ONW ST.

Presentation transcript:

Introduction (2/2) in Adaptive Cooperative Systems by Martine Beckerman, 1997 09’ 7.10 B.-W. Ku

1.4 Synaptic Plasticity

1.4 Synaptic Plasticity

1.4.1 Hebbian Synapses W ON W ON OFF

1.4.2 Experience-Dependent Modifications No development if electrical activity prevented.

1.5 Neural Assemblies

1.5 Neural Assemblies Neurons interacting with one another. Synchronous activity

1.5.1 Dynamic Adaptability Functional connectivity Stimulus-initiated Rapid (milliseconds ~ seconds) Dynamic Coding

1.5.2 Assembly Coding Individual neurons belong to different assemblies Feature binding Synchrony within One or more columns Columns in different cortical columns Analogous to cooperative labeling in Markov random field models