חישוביות וקוגניציה א' 06118. “Men ought to know that from nothing else but thence [from the brain] come joys, delights, laughter and sports, and sorrows,

Slides:



Advertisements
Similar presentations
FMRI Methods Lecture 10 – Using natural stimuli. Reductionism Reducing complex things into simpler components Explaining the whole as a sum of its parts.
Advertisements

Neural Network Toolbox COMM2M Harry R. Erwin, PhD University of Sunderland.
Neural Network I Week 7 1. Team Homework Assignment #9 Read pp. 327 – 334 and the Week 7 slide. Design a neural network for XOR (Exclusive OR) Explore.
Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne Swiss Federal Institute of Technology.
Biological and Artificial Neurons Michael J. Watts
Soft computing Lecture 6 Introduction to neural networks.
Class Web Site Go to ->current students - > class websites -> NEUR 3680A - >Class Websitewww.uleth.ca You will find the course outline as.
Neural NetworksNN 11 Neural Networks Teacher: Elena Marchiori R4.47 Assistant: Kees Jong S2.22
Biological inspiration Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to.
Introduction to Cognitive Science Sept 2005 :: Lecture #1 :: Joe Lau :: Philosophy HKU.
Un Supervised Learning & Self Organizing Maps Learning From Examples
CSE 153Modeling Neurons Chapter 2: Neurons A “typical” neuron… How does such a thing support cognition???
AN INTERACTIVE TOOL FOR THE STOCK MARKET RESEARCH USING RECURSIVE NEURAL NETWORKS Master Thesis Michal Trna
MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #29 4/12/02 Neural Networks.
COMP305. Part I. Artificial neural networks.. Topic 3. Learning Rules of the Artificial Neural Networks.
Neural Networks William Lai Chris Rowlett. What are Neural Networks? A type of program that is completely different from functional programming. Consists.
CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang.
Lyle Ungar, University of Pennsylvania Learning and Memory Reinforcement Learning.
Soft Computing Colloquium 2 Selection of neural network, Hybrid neural networks.
Neurons, Neural Networks, and Learning 1. Human brain contains a massively interconnected net of (10 billion) neurons (cortical cells) Biological.
MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way
Neuroscience: The last frontier of the biological sciences Understanding ourselves.
Neural mechanisms of Spatial Learning. Spatial Learning Materials covered in previous lectures Historical development –Tolman and cognitive maps the classic.
Presentation on Neural Networks.. Basics Of Neural Networks Neural networks refers to a connectionist model that simulates the biophysical information.
Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.
Pulsed Neural Networks Neil E. Cotter ECE Department University of Utah.
IE 585 Introduction to Neural Networks. 2 Modeling Continuum Unarticulated Wisdom Articulated Qualitative Models Theoretic (First Principles) Models Empirical.
Synesthesia: The Key to Understanding Language, Metaphor and Abstract Thought V. S. Ramachandran Center for Brain and Cognition University.
Advances in Modeling Neocortex and its impact on machine intelligence Jeff Hawkins Numenta Inc. VS265 Neural Computation December 2, 2010 Documentation.
NEURAL NETWORKS FOR DATA MINING
Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.
Neural coding (1) LECTURE 8. I.Introduction − Topographic Maps in Cortex − Synesthesia − Firing rates and tuning curves.
Artificial Neural Networks An Introduction. What is a Neural Network? A human Brain A porpoise brain The brain in a living creature A computer program.
Learning Styles and Brain-Based Learning September 10, 2013 Assoc.Prof.Dr. Chailerd Pichitpornchai M.D., Ph.D. President Sukhothai Thammathirat Open University.
Cognitive Science Overview Cognitive Science Defined The Brain Assumptions of Cognitive Science Cognitive Information Processing Cognitive Science and.
Introduction: Brain Dynamics Jaeseung Jeong, Ph.D Department of Bio and Brain Engineering, KAIST.
CS 478 – Tools for Machine Learning and Data Mining Perceptron.
The Brain And It’s Functions By G R. Lobes of the brain Parietal Lobe Occipital Lobe Temporal Lobe Frontal Lobe.
From brain activities to mathematical models The TempUnit model, a study case for GPU computing in scientific computation.
Neural Networks Presented by M. Abbasi Course lecturer: Dr.Tohidkhah.
Neural Networks. Molecules Levels of Information Processing in the Nervous System 0.01  m Synapses 1m1m Neurons 100  m Local Networks 1mm Areas /
Energy, Stereoscopic Depth, and Correlations. Molecules Levels of Information Processing in the Nervous System 0.01  m Synapses 1m1m Neurons 100 
Artificial Neural Networks (ANN). Artificial Neural Networks First proposed in 1940s as an attempt to simulate the human brain’s cognitive learning processes.
Spiking Neural Networks Banafsheh Rekabdar. Biological Neuron: The Elementary Processing Unit of the Brain.
Electrophysiology & fMRI. Neurons Neural computation Neural selectivity Hierarchy of neural processing.
Notes: 1. Extra Credit – Assignment 5 Due December Last exam December 10 Announcement and instructions on Class communication page 3. Spatial Abilities.
Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.
CSC321: Neural Networks Lecture 1: What are neural networks? Geoffrey Hinton
Neuroscience 1. Brain Organization 2. Single Neuron Computation 3. Six Organizational Principles of Neural Computation.
Chapter 2 Cognitive Neuroscience. Some Questions to Consider What is cognitive neuroscience, and why is it necessary? How is information transmitted from.
Lecture 12. Outline of Rule-Based Classification 1. Overview of ANN 2. Basic Feedforward ANN 3. Linear Perceptron Algorithm 4. Nonlinear and Multilayer.
Chapter 13 Artificial Intelligence. Artificial Intelligence – Figure 13.1 The Turing Test.
Fundamental ARTIFICIAL NEURAL NETWORK Session 1st
How do thought, emotion and behavior arise from an amorphous blob?
Real Neurons Cell structures Cell body Dendrites Axon
Joost N. Kok Universiteit Leiden
Functional segregation vs. functional integration of the brain
CSE 473 Introduction to Artificial Intelligence Neural Networks
Intelligent Leaning -- A Brief Introduction to Artificial Neural Networks Chiung-Yao Fang.
XOR problem Input 2 Input 1
Educational Psychology and Learning Systems
Intelligent Leaning -- A Brief Introduction to Artificial Neural Networks Chiung-Yao Fang.
Pulsed Neural Networks
OCNC Statistical Approach to Neural Learning and Population Coding ---- Introduction to Mathematical.
Synaptic integration.
ARTIFICIAL NEURAL networks.
Introduction to Neural Network
PYTHON Deep Learning Prof. Muhammad Saeed.
Machine Learning.
Presentation transcript:

חישוביות וקוגניציה א' 06118

“Men ought to know that from nothing else but thence [from the brain] come joys, delights, laughter and sports, and sorrows, griefs, despondency, and lamentations. And by this, in an special manner, we acquire wisdom and knowledge, and see and hear, and know what are foul and what are fair, what are bad and what are good, what are sweet and what unsavory.” Hippocrates, 460BC-370BC

What are the questions?

5.What is a neuronal map, how does it arise, and what is it good for? 6.What is the role of top-down connections? 8.What is the origin and functional properties of irregular activity? 11. What is the formal computation in early vision? 12. Are neurons adapted for specific computations? 13. How can neural systems compute in the time domain 14. How common are neural codes? 15. How does the hearing system perform auditory scene analysis? 22. Do qualia, metaphor, language, and abstract thought emerge from synesthesia, 23. What are the neural correlates of consciousness?

What are the challenges? The brain operates on multiple temporal and spatial scales (sec) sound localization Barn owl Ormia ochracea (fly) humans spike persistent activity protein synthesis protein turnover lifetime memories

What are the challenges? The brain operates on multiple temporal and spatial scales (m) ionic channel synapse soma of neuron fMRI resolution length of axon hypercolumn vesicle

The approach Proper level of abstraction Mathematical models

Mechanistic vs. normative questions 5.What is a neuronal map, how does it arise, and what is it good for? 6.What is the role of top-down connections? 8.What is the origin and functional properties of irregular activity? 11. What is the formal computation in early vision? 12. Are neurons adapted for specific computations? 13. How can neural systems compute in the time domain 14. How common are neural codes? 15. How does the hearing system perform auditory scene analysis? 22. Do qualia, metaphor, language, and abstract thought emerge from synesthesia, 23. What are the neural correlates of consciousness?

The approach Mechanistic models (‘how’ questions) Functional models (‘why’ questions’)

Syllabus Computation and Cognition A: Dynamics of neural networks Multistability Oscillations Associative memory Supervised learning Binary perceptron Linear perceptron Back-Propagation Unsupervised learning PCA Clustering Reinforcement learning

Syllabus Computation and Cognition B: Search algorithms Games Reinforcement learning Probabilistic models of Cognition Motor control

מטרות הקורס חישוביות וקוגניציה הקניית כלים חישוביים ותיאורטיים

חישוביות וקוגניציה א' מרצה : יונתן לוינשטיין מתרגל : איתמר לרנר

דרישות פורמליות (לא מחייב) נוכחות בכיתה ( לא פחות מ 50 %~ מהשעורים ) תרגילים יינתנו לשבועיים חובת הגשה 5/6 הגשת תרגילים בזמן ! ( 5 - נקודות לכל יום איחור ) 30 % מהציון הסופי בחינת סיום מחברות פתוחות 70 % מהציון