Computational Modeling of Cognitive Activity Prof. Larry M. Manevitz Course Slides: 2012.
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Transcript of Computational Modeling of Cognitive Activity Prof. Larry M. Manevitz Course Slides: 2012.
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Computational Modeling of Cognitive Activity
Prof. Larry M. Manevitz
Course Slides: 2012
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• 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
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Some Cognitive Tasks
• Memory– Declarative– Procedural– Associative
• Decision Making• Pattern Identification• Language• Speech• Reading• …
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• 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
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Related Fields, Related Methods?
• Neurophysiology– Cell Structure– System Biology
• Cognitive Psychology– Psychophysical Experiments
Important Tools fMRI EEG Others …
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• We have several tools from CS that can help us– Successfully model cognitive activity– Successfully explore activity in vivo (i.e. in a
person…)
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What tools are appropriate?
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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
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• 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.
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Examples of Neurons
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Neuron Stains
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• 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)
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L. Manevitz U. Haifa20
Definitions and History•McCullough –Pitts Neuron•Perceptron•Adaline•Linear Separability•Multi-Level Neurons•Neurons with Loops
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L. Manevitz U. Haifa21
Natural versus Artificial Neuron
•Natural NeuronMcCullough Pitts Neuron
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L. Manevitz U. Haifa22
Sample Feed forward Network (No loops)
Weights Weights
Weights
Input
Output
WjiVik
F( S wji xj