bioeng_admin
2009-01-11 16:25:24
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295
BiS400 Spring 2009
Special Topics on Biosystems: a computational theory of neural function
Lecturer: Christopher Fiorillo (fiorillo@kaist.ac.kr, 350-4326)
Time: Tues/Thurs, 1:00 - 2:15 PM
Classroom: #219
Course Description
The basic biophysical structure and function of the nervous system is now moderately well understood. However, our understanding of its computational function is minimal, and there had not been any general theory. The lack of a successful theory is evident in the lack of progress in building artificial neural systems that are able to match the intelligence of biological systems. I recently published a paper entitled “Towards a general theory of neural computation based on prediction by single neurons,” which is freely available at http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0003298. The course will be centered around this theory, which includes molecular, cellular and systems levels of analysis. The theory provides a synthesis of computational and theoretical principles with the known biophysical substrate of the nervous system. An understanding of the computational aspects of the theory requires very little knowledge of mathematics. I will provide the necessary background information on the underlying biology.
Credit 3 units (3:0:3)
Grading Mid-term Exam (35%), Final Exam (35%), Homework (30%)
Materials
There will not be any textbook of general use. Readings will be drawn from papers and books. See the web address above for more information.
Lecture Content
1. Theory Part 1: the computational goal of the nervous system: probability theory and information
2. Basic biophysical structure and function of the neuron
3. Theory Part 2: how a neuron can learn to predict: synaptic plasticity, plasticity of non-synaptic ion channels, reinforcement learning, organization of the system
4. Neuronal plasticity: computational and experimental work on synaptic and non-synaptic plasticity
5. Sensory Systems: the visual system, efficient coding, Bayesian inference
6. Reward Signals: how the system acquires information of relevance to its goals: dopamine, selective attention
BiS223 Biological Physics Spring 2009
Biological Physics
Lecturer: Dongsup Kim (kds@kaist.ac.kr, 869-4317)
Time: Mon/Wed/Fri, 2:00 - 2:50 PM
Classroom: Chung Moon Soul Bldg #219
Course Description
Biophysics is the branch of knowledge that applies the principles of physics and chemistry and the methods of mathematical analysis and computer modeling to understand how the mechanisms of biological systems work. This course introduces physical principles underlying the \"phenomenon life\" including structures and dynamics of biological systems on all levels of organization, and provides an opportunity to have own answers to the question \"what is life?\"
Credit 3 units (3:0:3)
Grading Mid-term Exam (30%), Final Exam (40%), Homework & Quiz (30%)
Textbook
1. \"Biological Physics: Energy, Information, Life\" written by Philip Nelson
(W.H. Freeman and Company, 2004)
2. Lecture notes, handouts, papers, web resources
Lecture Schedule
1. What is Biophysics (and Biological Physics)?
History of biophysics
2. Molecular structure of biological systems
Covalent, ionic, and hydrogen bonds.
Photo synthesis as a process of energy transfer
Activation energy, Debye Huckel theory
Mechanical properties of Biological membranes
3. Energetics and dynamics of biological systems
Thermodynamics: entropy, information, temperature,
Free energy, Sakur-Tetrode formula
Donnan equilibrium, osmotic pressure
Their biological applications
4. Diffusion, Dissipation, and Drive
Ideal gas law, Schrodinger\'s equations
Their biological applications
5. Random walks, Friction, and diffusion
Brownian motion and diffusion theory
Their biological applications
6. Life in the slow lane: The low Reynolds-number world
Friction in fluids, Laminar and turbulent structure, low Reynolds-number
Bacteria Flagella, vascular networks
7. Entropic forces at work
Poisson Boltzmann equations, Osotic pressure and surface tension
Their biological applications
8. Chemical forces and self-assembly
Special Topics on Biosystems: a computational theory of neural function
Lecturer: Christopher Fiorillo (fiorillo@kaist.ac.kr, 350-4326)
Time: Tues/Thurs, 1:00 - 2:15 PM
Classroom: #219
Course Description
The basic biophysical structure and function of the nervous system is now moderately well understood. However, our understanding of its computational function is minimal, and there had not been any general theory. The lack of a successful theory is evident in the lack of progress in building artificial neural systems that are able to match the intelligence of biological systems. I recently published a paper entitled “Towards a general theory of neural computation based on prediction by single neurons,” which is freely available at http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0003298. The course will be centered around this theory, which includes molecular, cellular and systems levels of analysis. The theory provides a synthesis of computational and theoretical principles with the known biophysical substrate of the nervous system. An understanding of the computational aspects of the theory requires very little knowledge of mathematics. I will provide the necessary background information on the underlying biology.
Credit 3 units (3:0:3)
Grading Mid-term Exam (35%), Final Exam (35%), Homework (30%)
Materials
There will not be any textbook of general use. Readings will be drawn from papers and books. See the web address above for more information.
Lecture Content
1. Theory Part 1: the computational goal of the nervous system: probability theory and information
2. Basic biophysical structure and function of the neuron
3. Theory Part 2: how a neuron can learn to predict: synaptic plasticity, plasticity of non-synaptic ion channels, reinforcement learning, organization of the system
4. Neuronal plasticity: computational and experimental work on synaptic and non-synaptic plasticity
5. Sensory Systems: the visual system, efficient coding, Bayesian inference
6. Reward Signals: how the system acquires information of relevance to its goals: dopamine, selective attention
BiS223 Biological Physics Spring 2009
Biological Physics
Lecturer: Dongsup Kim (kds@kaist.ac.kr, 869-4317)
Time: Mon/Wed/Fri, 2:00 - 2:50 PM
Classroom: Chung Moon Soul Bldg #219
Course Description
Biophysics is the branch of knowledge that applies the principles of physics and chemistry and the methods of mathematical analysis and computer modeling to understand how the mechanisms of biological systems work. This course introduces physical principles underlying the \"phenomenon life\" including structures and dynamics of biological systems on all levels of organization, and provides an opportunity to have own answers to the question \"what is life?\"
Credit 3 units (3:0:3)
Grading Mid-term Exam (30%), Final Exam (40%), Homework & Quiz (30%)
Textbook
1. \"Biological Physics: Energy, Information, Life\" written by Philip Nelson
(W.H. Freeman and Company, 2004)
2. Lecture notes, handouts, papers, web resources
Lecture Schedule
1. What is Biophysics (and Biological Physics)?
History of biophysics
2. Molecular structure of biological systems
Covalent, ionic, and hydrogen bonds.
Photo synthesis as a process of energy transfer
Activation energy, Debye Huckel theory
Mechanical properties of Biological membranes
3. Energetics and dynamics of biological systems
Thermodynamics: entropy, information, temperature,
Free energy, Sakur-Tetrode formula
Donnan equilibrium, osmotic pressure
Their biological applications
4. Diffusion, Dissipation, and Drive
Ideal gas law, Schrodinger\'s equations
Their biological applications
5. Random walks, Friction, and diffusion
Brownian motion and diffusion theory
Their biological applications
6. Life in the slow lane: The low Reynolds-number world
Friction in fluids, Laminar and turbulent structure, low Reynolds-number
Bacteria Flagella, vascular networks
7. Entropic forces at work
Poisson Boltzmann equations, Osotic pressure and surface tension
Their biological applications
8. Chemical forces and self-assembly
