Storage capacity: consider the neocortex ~20*10^9 cells, 20*10^13 synapses.

Slides:



Advertisements
Similar presentations
Synapses and neurotransmitters
Advertisements

History, Part III Anatomical and neurochemical correlates of neuronal plasticity Two primary goals of learning and memory studies are –i. neural centers.
Cellular and Molecular Basis of Memory Engram Temporal Types of Memory
Long-term Potentiation as a Physiological Phenomenon
Activity-Dependent Development I April 23, 2007 Mu-ming Poo 1.Development of OD columns 2.Effects of visual deprivation 3. The critical period 4. Hebb’s.
Justin Besant BIONB 2220 Final Project
Spike Timing-Dependent Plasticity Presented by: Arash Ashari Slides mostly from: 1  Woodin MA, Ganguly K, and Poo MM. Coincident pre-
Spike timing-dependent plasticity: Rules and use of synaptic adaptation Rudy Guyonneau Rufin van Rullen and Simon J. Thorpe Rétroaction lors de l‘ Intégration.
Spike timing-dependent plasticity Guoqiang Bi Department of Neurobiology University of Pittsburgh School of Medicine.
Plasticity in the nervous system Edward Mann 17 th Jan 2014.
Chapter 7 Supervised Hebbian Learning.
Synapses are everywhere neurons synapses Synapse change continuously –From msec –To hours (memory) Lack HH type model for the synapse.
Long term potentiation (LTP) of an excitatory synaptic inputs is input specific.
Supervised Hebbian Learning. Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing.
When an axon of cell A is near enough to excite cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change.
Artificial neural networks.
DAVID SANTUCCI QUANTITATIVE BIOLOGY BOOTCAMP 2009 A BRIEF HISTORY OF THE SYNAPSE.
1 Activity-dependent Development (2) Hebb’s hypothesis Hebbian plasticity in visual system Cellular mechanism of Hebbian plasticity.
PART 5 Supervised Hebbian Learning. Outline Linear Associator The Hebb Rule Pseudoinverse Rule Application.
COMP305. Part I. Artificial neural networks.. Topic 3. Learning Rules of the Artificial Neural Networks.
Critical periods A time period when environmental factors have especially strong influence in a particular behavior. –Language fluency –Birds- Are you.
Synaptic plasticity Basic Neuroscience NBL 120. classical conditioning CS (neutral) - no response US - UR After pairing: CS - CR.
PSY105 Neural Networks 4/5 4. “Traces in time” Assignment note: you don't need to read the full book to answer the first half of the question. You should.
Learning rules in the hippocampus and cerebellum Sam Wang Princeton University synapse.princeton.edu.
Molecular mechanisms of memory. How does the brain achieve Hebbian plasticity? How is the co-activity of presynaptic and postsynaptic cells registered.
Neural Plasticity: Long-term Potentiation Lesson 15.
HEBB’S THEORY The implications of his theory, and their application to Artificial Life.
synaptic plasticity is the ability of the connection, or synapse, between two neurons to change in strength in response to either use or disuse of transmission.
Neural Networks and Fuzzy Systems Hopfield Network A feedback neural network has feedback loops from its outputs to its inputs. The presence of such loops.
7 1 Supervised Hebbian Learning. 7 2 Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part.
Unsupervised learning
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.
Spike-timing-dependent plasticity (STDP) and its relation to differential Hebbian learning.
Mechanisms for memory: Introduction to LTP Bailey Lorv Psych 3FA3 November 15, 2010.
1960s, 1970s, converging evidence from cognitive neuropsychology, psychology, neurobiology support the view of Multiple memory systems, efforts to experimentally.
The Action Potential & Impulse/Signal Propagation Learning Objective Be able to describe what a synapse is. Be able to describe how an action potential.
How the Brain Learns: Rules and Outcomes Psychology 209 January 28, 2013.
1 Financial Informatics –XVII: Unsupervised Learning 1 Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2,
Strong claim: Synaptic plasticity is the only game in town. Weak Claim: Synaptic plasticity is a game in town. Biophysics class: section III The synaptic.
Synaptic plasticity DENT/OBHS 131 Neuroscience 2009.
James L. McClelland Stanford University
UNIT 3 THE CONSCIOUS SELF
Synaptic plasticity. Definition Alteration of synapse response to input.
Dopamine (DA) neuron Cell body (Soma) terminals axons Dendrites.
Copyright © 2009 Allyn & Bacon How Your Brain Stores Information Chapter 11 Learning, Memory, and Amnesia.
Neural Mechanisms of Learning & Memory Lesson 24.
Fear conditioning… e.g., Electric shock associated with specific stimuli.
0 Chapter 4: Associators and synaptic plasticity Fundamentals of Computational Neuroscience Dec 09.
Perceptron vs. the point neuron Incoming signals from synapses are summed up at the soma, the biological “inner product” On crossing a threshold, the cell.
Synaptic Plasticity Synaptic efficacy (strength) is changing with time. Many of these changes are activity-dependent, i.e. the magnitude and direction.
Do Now Complete Part 1 on your worksheets with a partner. A problem for you to solve: – Given that you know the axon sends signals electrically, and that.
Brain Communication. The Adolescent Brain Pre-frontal cortex still a work in progress: – Decision making based on reward rather than risk Awesomely.
Synaptic Transmission / Central Synapses I Tom O’Dell Department of Physiology C8-161 (NPI), x64654.
Ch 8. Synaptic Plasticity 8.9 ~ 8.10 Adaptive Cooperative Systems, Martin Beckerman, Summarized by Kim, S. –J. Biointelligence Laboratory, Seoul.
Spike-timing-dependent plasticity (STDP) and its relation to differential Hebbian learning.
Long Term Potentiation
Cell communication III: the nerve system
Financial Informatics –XVII: Unsupervised Learning
Hebb and Perceptron.
Corso su Sistemi complessi:
Transmission of Action Potential Across a Synapse
Spike timing dependent plasticity
Synapse.
Neuron to Neuron Impulse Movement
How are synaptic vesicles clustered?
Supervised Hebbian Learning
David C. Spanswick, Stephanie E. Simonds, Michael A. Cowley 
Spike-timing-dependent plasticity (STDP)
Nomadic AMPA Receptors and LTP
Presentation transcript:

Storage capacity: consider the neocortex ~20*10^9 cells, 20*10^13 synapses

Hebb’s postulate “When an axon of cell A is near enough to excite cell B and 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.” From the “Organization of Behavior” by D. O. Hebb (1949) Cells that fire toghether are wired together

Stent’s postulate “ When an axon of cell A repeatedly and persistently fails to excite the postsynaptic cell B while cell B is firing under the influence of other presynaptic axons, metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is decreased.” Cells that fire out of sinc lose their link

NMDA receptor is the “coincidence detector”

Rapid spine delivery and redistribution of AMPA receptors after synaptic NMDA receptor activation. By Shi et al. (1999) Science 284: