'Hebbian learning' diaporamas de présentation

Models of Learning

1.03k views • 84 slides

Learning and Synaptic P lasticity

846 views • 69 slides

Pre-processing

Pre-processing. X O X O X O. X X X O O O. Output data. Post-processing. Idea:. Network. Pre-processing is good to use with networks since the network training => pre-processing does not need to be exact. Pre-processing. Input data. Why Pre-process?

875 views • 63 slides

20 Minute Quiz

20 Minute Quiz. For each of the questions, you can use text, diagrams, bullet points, etc. Give three processes that keep neural computation from proceeding faster than at millisecond scale. How does pre-natal activity dependent visual tuning work?

955 views • 48 slides

Neural Networks ( ??????? ) BY ?????

Neural Networks ( ??????? ) BY ?????. 1) What is a neural network ?

615 views • 48 slides

Universal Learning Models

Cognitive Neuroscience and Embodied Intelligence. Universal Learning Models. Based on a courses taught by Prof. Randall O'Reilly , University of Colorado, Prof. W?odzis?aw Duch , Uniwersytet Miko?aja Kopernika and http://wikipedia.org/.

505 views • 38 slides

Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning

Lecture 8. Artificial neural networks:. Unsupervised learning. Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning Self-organising computational map: Kohonen network Summary. Introduction.

1.64k views • 37 slides

Universal Learning Models

Computational Intelligence. Universal Learning Models. Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. W?odzis?aw Duch Uniwersytet Miko?aja Kopernika. Janusz A. Starzyk.

451 views • 34 slides

Language

Cognitive Neuroscience and Embodied Intelligence. Language. Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars courses taught by Prof. Randall O'Reilly , University of Colorado, and Prof. W?odzis?aw Duch , Uniwersytet Miko?aja Kopernika and http://wikipedia.org/.

460 views • 33 slides

4. Associators and Synaptic Plasticity

4. Associators and Synaptic Plasticity. Fundamentals of Computational Neuroscience, T. P. Trappenberg, 2010. Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering

419 views • 28 slides

Self-organization and error correction

Computational Intelligence. Self-organization and error correction. Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. W?odzis?awa Ducha Uniwersytet Miko?aja Kopernika. Janusz A. Starzyk. How should an ideal learning system look?

394 views • 27 slides

Biological Modeling of Neural Networks

Week 6 : Hebbian Learning. 6 .1 Synaptic Plasticity - Hebbian Learning - Short- term Plasticity - Long- term Plasticity - Reinforcement Learning 6 .2 Models of synaptic plasticity - Hebbian learning rules 6 .3 - - 6.4.

502 views • 27 slides

Self Organization: Hebbian Learning

452 views • 24 slides

Neural Networks (NN) Part 1

Neural Networks (NN) Part 1. NN: Basic Ideas Computational Principles Examples of Neural Computation. 1. NN: Basic Ideas. Neural network: An example. A neural network has been shown to be a universal Turing machine ; it can compute anything that is computable (Siegelmann & Sontag, 1991).

391 views • 23 slides

A globally asymptotically stable plasticity rule for firing rate homeostasis

A globally asymptotically stable plasticity rule for firing rate homeostasis. Prashant Joshi & Jochen Triesch. Email: { joshi,triesch}@ fias.uni-frankfurt.de | Web: www.fias.uni-frankfurt.de/~{joshi,triesch}. Synopsis.

319 views • 18 slides

Computational Cognitive Neuroscience Lab

Computational Cognitive Neuroscience Lab. Today: Model Learning. Computational Cognitive Neuroscience Lab. Today: Homework is due Friday, Feb 17 Chapter 4 homework is shorter than the last one! Undergrads omit 4.4, 4.5, 4.7c, 4.7d. Hebbian Learning.

274 views • 14 slides

Lecture 6. Learning (III): Associative Learning and Principal Component Analysis

Lecture 6. Learning (III): Associative Learning and Principal Component Analysis. Outline. Associative Learning: Hebbian Learning Use Associative Learning to compute Principal Component. Associative (Hebbian) Learning.

413 views • 11 slides

Lecture 6. Learning (III): Associative Learning and Principal Component Analysis

Lecture 6. Learning (III): Associative Learning and Principal Component Analysis. Outline. Associative Learning: Hebbian Learning Use Associative Learning to compute Principal Component. Associative (Hebbian) Learning.

263 views • 11 slides

The search for organizing principles of brain function

The search for organizing principles of brain function. Needed at multiple levels: synapse => cell => brain area (cortical maps) => hierarchy of areas Self-organization: Hebbian learning => feature-analyzing cells => cortical maps

225 views • 11 slides

Plan Submitted By : Rahul Jaitly

206 views • 8 slides