白塊線上課程

week1.Basic neuronal models

Part 01 Introduction(1)

1m 19s

lecture_part 01

1m 19s

Part 02 Membrane potential and Nernst equation

1m 19s

lecture_part 02

1m 19s

Part 03 Goldman equation and Leaky integtate-and-fire model(1)

1m 19s

Part 03 Goldman equation and Leaky integtate-and-fire model(2)

1m 19s

Part 03 Goldman equation and Leaky integtate-and-fire model(3)

1m 19s

lecture_part 03

1m 19s

Part 04 LIF model-stability(1)

1m 19s

Part 04 LIF model-stability(2)

1m 19s

lecture_part 04

1m 19s

Part 05 LIF model-Firing rate

1m 19s

lecture_part 05

1m 19s

Quiz1

1m 19s
week2.Synapse and channel dynamics

Part 06 Synapses

1m 19s

lecture_part 06

1m 19s

Part 07 Synapses

1m 19s

Part 07 Synapses

1m 19s

lecture_part 07

1m 19s

Part 08 Synapses

1m 19s

lecture_part 08

1m 19s

Part 09 Hodgkin-Huxley model The dynamics of channels(1)

1m 19s

Part 09 Hodgkin-Huxley model The dynamics of channels(2)

1m 19s

lecture_part 09

1m 19s

Part 10 Hodgkin-Huxley model The generation of action potentials(1)

1m 19s

Part 10 Hodgkin-Huxley model The generation of action potentials(2)

1m 19s

lecture_part 10

1m 19s

Quiz2

1m 19s
week3.Signal propagation in neurons

Part 11 Signal propagation in single neurons cable equation

1m 19s

lecture_part 11

1m 19s

Part 12 Signal propagation in single neurons dendrites(1)

1m 19s

Part 12 Signal propagation in single neurons dendrites(2)

1m 19s

lecture_part 12

1m 19s

Part 13 Signal propagation in single neurons axon(1)

1m 19s

Part 13 Signal propagation in single neurons axon(2)

1m 19s

Part 13 Signal propagation in single neurons axon(3)

1m 19s

lecture_part 13

1m 19s

Quiz3

1m 19s
week4.Neural network simulators

Part 14 Neural network simulators(1)

1m 19s

Part 14 Neural network simulators(2)

1m 19s

Part 14 Neural network simulators(3)

1m 19s

Part 14 Neural network simulators(4)

1m 19s

lecture_part 14

1m 19s

Quiz4

1m 19s
week5.Basics of dynamical systems

Part 15 Stability(1)

1m 19s

Part 15 Stability(2)

1m 19s

lecture_part 15

1m 19s

Part 16 Bifurcation(1)

1m 19s

Part 16 Bifurcation(2)

1m 19s

lecture_part 16

1m 19s

Part 17 Matrix analysis and linear algebra(1)

1m 19s

Part 17 Matrix analysis and linear algebra(2)

1m 19s

lecture_part 17

1m 19s

Quiz5

1m 19s
week6.Firing rate model and networks

Part 18 Firing rate model， binary model and small networks(1)

1m 19s

Part 18 Firing rate model， binary model and small networks(2)

1m 19s

lecture_part 18

1m 19s

Part 19 Stability in two-dimensional systems(1)

1m 19s

Part 19 Stability in two-dimensional systems(2)

1m 19s

lecture_part 19

1m 19s

Part 20 Recurrent network-states and stability(1)

1m 19s

Part 20 Recurrent network-states and stability(2)

1m 19s

Part 20 Recurrent network-states and stability(3)

1m 19s

lecture_part 20

1m 19s

Quiz6

1m 19s
week7.Memory and plasticity

Part 21 Memory(1)

1m 19s

Part 21 Memory(2)

1m 19s

Part 21 Memory(3)

1m 19s

Part 21 Memory(4)

1m 19s

lecture_part 21

1m 19s

Part 22 Oscillation

1m 19s

lecture_part 22

1m 19s

Part 23 Synaptic plasticity The basics(1)

1m 19s

Part 23 Synaptic plasticity The basics(2)

1m 19s

lecture_part 23

1m 19s

Quiz7

1m 19s
week8.Learning

Part 24 Hebbian learning The implementation(1)

1m 19s

Part 24 Hebbian learning The implementation(2)

1m 19s

lecture_part 24

1m 19s

Part 25 Unsupervised learning(1)

1m 19s

Part 25 Unsupervised learning(2)

1m 19s

lecture_part 25

1m 19s

Part 26 Reinforcement learning Classical conditioning(1)

1m 19s

Part 26 Reinforcement learning Classical conditioning(2)

1m 19s

Part 26 Reinforcement learning Classical conditioning(3)

1m 19s

Part 26 Reinforcement learning Classical conditioning(4)

1m 19s

lecture_part 26

1m 19s

Quiz8

1m 19s
week9.Operant conditioning and decision making

Part 27 Reinforcement learning Operant conditioning(1)

1m 19s

Part 27 Reinforcement learning Operant conditioning(2)

1m 19s

Part 27 Reinforcement learning Operant conditioning(3)

1m 19s

lecture_part 27

1m 19s

Part 28 Decision making - Basic theory and Diffusion models

1m 19s

lecture_part 28

1m 19s

Part 29 Decision making Attractor circuits and working memory

1m 19s

lecture_part 29

1m 19s

Part 30 Decision making Winner-take-all dynamics and decision networks

1m 19s

lecture_part 30

1m 19s

Quiz9

1m 19s
計算神經科學課程總結

計算神經科學課程總結

1m 19s

Course Descriptions

計算神經科學是一門以物理數學原理來描述大腦運作的科學。它上接認知科學下接分子生物，是跨領域合作的結晶。在腦科學成為21世紀顯學的同時，計算神經科學也有著無限的潛力。

本課程提供線上測驗，但不提供線上成績單，僅提供成績顯示。

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10 hours video
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羅中泉

國立清華大學生命科學系教授

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聯絡方式：cclo@mx.nthu.edu.tw

研究專長

計算神經科學、神經資訊學

認知行為（cognitive behavior）與生理功能（physiological functions）的神經迴路機制。

學歷

波士頓大學物理博士

布蘭戴斯大學與耶魯大學 Dr. Xiao-Jing Wang 的實驗室完成博士後研究。

經歷

國立清華大學系統神經科學研究所教授兼所長

2008年8月加入國立清華大學生物資訊與結構生物研究所

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Guests

2024-自學課程-計算神經科學(1-6月)

Course Descriptions

How students rated this courses

4.5

Reviews

What’s included

羅中泉

11,604

32

12,230