Neurophysiology and Information
- 한국과학기술원
- Christopher Fiorillo
페이스북
트위터
카카오톡
네이버밴드
링크복사
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- 주제분류
- 자연과학 >생물ㆍ화학ㆍ환경 >생명과학
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- 강의학기
- 2010년 1학기
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- 조회수
- 8,125
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This course will focus on the organization and flow of information within the nervous system, at the levels of molecules, single neurons, and systems 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 a review of cellular and systems neurophysiology. However, students are expected to have already taken a course that covers basic neurobiology. Only a minimal knowledge of mathematics is needed.
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 a review of cellular and systems neurophysiology. However, students are expected to have already taken a course that covers basic neurobiology. Only a minimal knowledge of mathematics is needed.
- 수강안내 및 수강신청
- ※ 수강확인증 발급을 위해서는 수강신청이 필요합니다
Introduction : Neurophysiology and Information
차시별 강의
| 1. | |
Introduction : Neurophysiology and Information | The contents of this lecture will include intersection of neurophysiology and information. |  |
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Introduction : Neurophysiology and Information - Lecture note | The contents of this lecture will include intersection of neurophysiology and information. |  |
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| 2. | |
Discussion : “Why we have not made significant progress towards artificial intelligence” | The contents of this lecture will include development of Artificial Intelligence and students' opinion about that. | |
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Discussion : “Why we have not made significant progress towards artificial intelligence” | The contents of this lecture will include development of Artificial Intelligence and students' opinion about that. | |
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| 3. | |
Information and Probability 1 | The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. | |
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Information and Probability 1 - Lecture note 1 | The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. | |
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Information and Probability 1 - Lecture note 2 | The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. | |
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Information and Probability 2 | The contents of this lecture will include (1) the definition of probability of frequentist and bayesian and (2) probability distributions related to the Maximum Entropy Principle. | |
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Information and Probability 3 | The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. | |
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Information and Probability 3 - Lecture note 1 | The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. | |
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Information and Probability 3 - Lecture note 2 | The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. | |
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Information and Probability 4 | The contents of this lecture will include (1) paper about "Bayesian intergration in sensorimotor learning" and (2) basic chemical concepts related on neurophysiology | |
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| 4. | |
A Neuron's Membrane Voltage 1 | The contents of this lecture will include (1) the basic concepts about equilibrium potential and (2) action potential. | |
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A Neuron's Membrane Voltage 1 - Lecture note 1 | The contents of this lecture will include (1) the basic concepts about equilibrium potential and (2) action potential. | |
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A Neuron's Membrane Voltage 2 | The contents of this lecture will include (1) the structures and (2) key properties of voltage gated Ion channels. | |
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| 5. | |
Synaptic Transmission | The contents of this lecture will include (1) the types and properties of synapses, (2) synaptic vesicle release, and (3) synaptic plasticity. | |
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Synaptic Transmission - Lecture note | The contents of this lecture will include (1) the types and properties of synapses, (2) synaptic vesicle release, and (3) synaptic plasticity. | |
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| 6. | |
Efficient Coding of Sensory Information | The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron | |
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Efficient Coding of Sensory Information - Lecture note | The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron | |
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Efficiency and ambiguity in an adaptive neural code | The contents of this lecture will include (1) statistical adaptation in neural code and (2) input/output relations for information transmission. | |
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General Theory | The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. | |
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General Theory - Lecture note | The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. | |
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General Theory 2 | The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. | |
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General Theory 4 | The contents of this lecture will include (1) How a neuron could select sources, (2) predicting future reward, and (3) plasticity rule. | |
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General Theory 3 | The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. | |
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theory - lecture note | The contents of this lecture will include (1) physiological principals of the retina. | |
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| 9. | |
Retina 1 | The contents of this lecture will include (1) physiological principals of the retina. | |
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Retina 2 | The contents of this lecture will include (1) physiological principals of the retina , (2) predictive coding | |
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| 10. | |
The Central Visual System 1 | The contents of this lecture will include principles of (1) the Retinofugal Projection and (2) The Lateral Geniculate Nucleus (LGN) | |
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The Central Visual System 2 | The contents of this lecture will include anatomy and principles of the Striate Cortex | |
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The Central Visual System ( lecture note) | |
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| 11. | |
snaptic plasticity1-1 | The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity | |
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synaptic plasticity(lecture note) | |
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snaptic plasticity1-2 | The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity | |
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| 12. | |
snaptic plasticity2-1 | The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) | |
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snaptic plasticity2-2 | The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) | |
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| 13. | |
Reward Signal 1 | |
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Reward Signal 2 | The contents of this lecture will include (1) dopamine's role and efficient representation, and (2) the principles of attention. | |
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Reward Signal 3 and Discussion | The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. | |
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Reward Signal (lecture note) | |
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| 14. | |
Motor System | The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. | |
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Motor System(Lecture note) | |
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