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Transcript
Computational Modeling of Cognitive Activity Prof. Larry M. Manevitz Course Slides: 2012 • Aim of Course: To view some of the methods and results of Cognitive and Brain Modeling – What is it good for? – What does it replace? • Aim of Course: To understand how machine learning methods could help understand cognition and Brain Function Some Cognitive Tasks • Memory – Declarative – Procedural – Associative • • • • • • Decision Making Pattern Identification Language Speech Reading … • What is the brain? • What is cognition? – We can think of brain activity as a computation. – Can we do “reverse engineering”? – Can we explain activity? • Many approaches – Physiological Explanations – Computational Explanations – Cognitive Explanations Related Fields, Related Methods? • Neurophysiology – Cell Structure – System Biology • Cognitive Psychology – Psychophysical Experiments Important Tools fMRI EEG Others … • We have several tools from CS that can help us – Successfully model cognitive activity – Successfully explore activity in vivo (i.e. in a person…) What tools are appropriate? Basic Structure of Neural System • From work of Cajal (as opposed to Golgi), the neural system is separated into discrete cells. • So we need to understand at least – Computational aspect of separate cells – Computational aspect of network of cells • Let’s start with the simplest model. • The neural system is known to be cellular. That means there is not continuous connections (like an electric wire) but discrete elements. Examples of Neurons Neuron Stains • Let’s start in 1948. McCullough Pitts. Want to understand how the brain works. • Their view: all there is, is Boolean functions. • Note: Same view as logicians as old as Boole (1898) Definitions and History McCullough –Pitts Neuron Perceptron Adaline Linear Separability Multi-Level Neurons Neurons with Loops 20 L. Manevitz U. Haifa • • • • • • Natural versus Artificial Neuron McCullough Pitts Neuron 21 L. Manevitz U. Haifa Natural Neuron • Sample Feed forward Network (No loops) Weights Weights Output Weights Input Wji Vik F(S wji xj 22 L. Manevitz U. Haifa
