Neurophysiology and Information<Theory of Brain Function>

한국과학기술원
Christopher Fiorillo

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

자연과학 >생물ㆍ화학ㆍ환경 >생명과학
강의학기

2013년 1학기

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6,033

This course will introduce the general theory of brain function. It will focus on the organization and flow of information within the nervous system, at the levels of molecules, single neurons, and networks of neurons. The basic biophysical structure and function of the nervous system is now moderately well understood. However, the textbooks that

describe the biology of the nervous system generally do not discuss information, even though it is universally agreed that the nervous system is an information processing system. The course will relate the biophysical structure and function of the nervous system to the processing of information. In doing so, the course will discuss the fundamental principles of the nervous system that should be helpful in designing intelligent machines that are capable of unsupervised learning. The course will include aspects of cellular and systems neurophysiology. Students are expected to have already taken a course that covers basic neurobiology, although past students without a background in neurobiology have done moderately well. Only a minimal knowledge of mathematics is needed.

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overview (1)

차시별 강의

1.	Introduction and overview	1. Introduction of this course
2.	overview	Theory of Brain Function
	overview (1)	Theory of Brain Function
	overview (2)	Theory of Brain Function
	overview (3)	Theory of Brain Function
3.	Inference	Inference in Perception, Cognition, and Motor Control
	Inference (1)	Inference in Perception, Cognition, and Motor Control
	Inference (2)	Inference in Perception, Cognition, and Motor Control
4.	Conventional logic & information	1. Information flows through molecular sensors 2. Information transformation in sensory-motor pathway
	Conventional logic & information	1. Information flows through molecular sensors 2. Information transformation in sensory-motor pathway
5.	Frequentist Probability Theory	1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability
	Frequentist Probability Theory (1)	1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability
	Frequentist Probability Theory (2)	1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability
6.	Jayness Probability Theory	1. Understanding Bayes theorem 2. Derivation and Application
	Jayness Probability Theory	1. Understanding Bayes theorem 2. Derivation and Application
7.	Probability and the Brain	1. Janesian observers 2. B.F Skinner and Behaviorism 3.Theory of Mind
	Probability and the Brain	1. Janesian observers 2. B.F Skinner and Behaviorism 3.Theory of Mind
8.	Logic in the Brain	1. Logic as Prescription and Description 2. Sequential logic
	Logic in the Brain	1. Logic as Prescription and Description 2. Sequential logic
9.	Efficient Coding	1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information
	Efficient Coding (1)	1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information
	Efficient Coding (2)	1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information
10.	Prediction Error 1: Introduction	1. A neuron has information about its stimulus 2. Input and Output of Neuron
	Prediction Error 1: Introduction (1)	1. A neuron has information about its stimulus 2. Input and Output of Neuron
	Prediction Error 1: Introduction (2)	1. A neuron has information about its stimulus 2. Input and Output of Neuron
11.	Prediction Error 2: Mechanisms	1. Prediction error in a single neuron 2. Information and Correlation
	Prediction Error 2: Mechanisms (1)	1. Prediction error in a single neuron 2. Information and Correlation
	Prediction Error 2: Mechanisms (2)	1. Prediction error in a single neuron 2. Information and Correlation
	Prediction Error 2: Mechanisms (3)	1. Prediction error in a single neuron 2. Information and Correlation
12.	Theory of Learning	Neurons inputs and Diversity of ion channels
	Theory of Learning (1)	Neurons inputs and Diversity of ion channels
	Theory of Learning (2)	Neurons inputs and Diversity of ion channels
13.	Mechanisms of Leaning	Mechanisms for learning and LTP / LTD
	Mechanisms of Leaning	Mechanisms for learning and LTP / LTD
14.	Visual System	1. Physiology of Striate Cortex 2. Mechanism of Forming Receptive fields
	Visual System	1. Physiology of Striate Cortex 2. Mechanism of Forming Receptive fields
15.	Reward and Attention	Learning to predict reward and 3 term hebbain rule and spike timing depedent plasticity
	Reward and Attention	Learning to predict reward and 3 term hebbain rule and spike timing depedent plasticity