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- 주제분류
- 자연과학 >생물ㆍ화학ㆍ환경 >생명과학
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- 강의학기
- 2013년 1학기
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- 조회수
- 7,747
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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.
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.
- 수강안내 및 수강신청
- ※ 수강확인증 발급을 위해서는 수강신청이 필요합니다
차시별 강의
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Introduction and overview | 1. Introduction of this course | ![]() |
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overview | Theory of Brain Function | ![]() |
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overview (1) | Theory of Brain Function | |
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overview (2) | Theory of Brain Function | |
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overview (3) | Theory of Brain Function | |
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Inference | Inference in Perception, Cognition, and Motor Control | ![]() |
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Inference (1) | Inference in Perception, Cognition, and Motor Control | |
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Inference (2) | Inference in Perception, Cognition, and Motor Control | |
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Conventional logic & information | 1. Information flows through molecular sensors 2. Information transformation in sensory-motor pathway |
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Conventional logic & information | 1. Information flows through molecular sensors 2. Information transformation in sensory-motor pathway |
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Frequentist Probability Theory | 1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability |
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Frequentist Probability Theory (1) | 1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability |
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Frequentist Probability Theory (2) | 1. Importance of Probability and Philosophy 2. Bayesian probability and Frequentist probability |
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Jayness Probability Theory | 1. Understanding Bayes theorem 2. Derivation and Application |
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Jayness Probability Theory | 1. Understanding Bayes theorem 2. Derivation and Application |
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Probability and the Brain | 1. Janesian observers 2. B.F Skinner and Behaviorism 3.Theory of Mind |
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Probability and the Brain | 1. Janesian observers 2. B.F Skinner and Behaviorism 3.Theory of Mind |
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Logic in the Brain | 1. Logic as Prescription and Description 2. Sequential logic |
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Logic in the Brain | 1. Logic as Prescription and Description 2. Sequential logic |
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Efficient Coding | 1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information |
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Efficient Coding (1) | 1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information |
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Efficient Coding (2) | 1. Free Energy Principle 2. Baysian inference 3. Efficient coding and mutual information |
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Prediction Error 1: Introduction | 1. A neuron has information about its stimulus 2. Input and Output of Neuron |
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Prediction Error 1: Introduction (1) | 1. A neuron has information about its stimulus 2. Input and Output of Neuron |
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Prediction Error 1: Introduction (2) | 1. A neuron has information about its stimulus 2. Input and Output of Neuron |
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Prediction Error 2: Mechanisms | 1. Prediction error in a single neuron 2. Information and Correlation |
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Prediction Error 2: Mechanisms (1) | 1. Prediction error in a single neuron 2. Information and Correlation |
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Prediction Error 2: Mechanisms (2) | 1. Prediction error in a single neuron 2. Information and Correlation |
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Prediction Error 2: Mechanisms (3) | 1. Prediction error in a single neuron 2. Information and Correlation |
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Theory of Learning | Neurons inputs and Diversity of ion channels | ![]() |
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Theory of Learning (1) | Neurons inputs and Diversity of ion channels | |
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Theory of Learning (2) | Neurons inputs and Diversity of ion channels | |
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Mechanisms of Leaning | Mechanisms for learning and LTP / LTD | ![]() |
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Mechanisms of Leaning | Mechanisms for learning and LTP / LTD | |
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Visual System | 1. Physiology of Striate Cortex 2. Mechanism of Forming Receptive fields |
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Visual System | 1. Physiology of Striate Cortex 2. Mechanism of Forming Receptive fields |
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Reward and Attention | Learning to predict reward and 3 term hebbain rule and spike timing depedent plasticity | ![]() |
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Reward and Attention | Learning to predict reward and 3 term hebbain rule and spike timing depedent plasticity | |
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