章節

• week1.Basic neuronal models
  • ● Part 01 Introduction(1)
  • ● lecture_part 01
  • ● Part 02 Membrane potential and Nernst equation
  • ● lecture_part 02
  • ● Part 03 Goldman equation and Leaky integtate-and-fire model(1)
  • ● Part 03 Goldman equation and Leaky integtate-and-fire model(2)
  • ● Part 03 Goldman equation and Leaky integtate-and-fire model(3)
  • ● lecture_part 03
  • ● Part 04 LIF model-stability(1)
  • ● Part 04 LIF model-stability(2)
  • ● lecture_part 04
  • ● Part 05 LIF model-Firing rate
  • ● lecture_part 05
  • ● Quiz1
• week2.Synapse and channel dynamics
  • ● Part 06 Synapses
  • ● lecture_part 06
  • ● Part 07 Synapses
  • ● Part 07 Synapses
  • ● lecture_part 07
  • ● Part 08 Synapses
  • ● lecture_part 08
  • ● Part 09 Hodgkin-Huxley model  The dynamics of channels(1)
  • ● Part 09 Hodgkin-Huxley model  The dynamics of channels(2)
  • ● lecture_part 09
  • ● Part 10 Hodgkin-Huxley model  The generation of action potentials(1)
  • ● Part 10 Hodgkin-Huxley model  The generation of action potentials(2)
  • ● lecture_part 10
  • ● Quiz2
• week3.Signal propagation in neurons
  • ● Part 11 Signal propagation in single neurons  cable equation
  • ● lecture_part 11
  • ● Part 12 Signal propagation in single neurons  dendrites(1)
  • ● Part 12 Signal propagation in single neurons  dendrites(2)
  • ● lecture_part 12
  • ● Part 13 Signal propagation in single neurons  axon(1)
  • ● Part 13 Signal propagation in single neurons  axon(2)
  • ● Part 13 Signal propagation in single neurons  axon(3)
  • ● lecture_part 13
  • ● Quiz3
• week4.Neural network simulators
  • ● Part 14 Neural network simulators(1)
  • ● Part 14 Neural network simulators(2)
  • ● Part 14 Neural network simulators(3)
  • ● Part 14 Neural network simulators(4)
  • ● lecture_part 14
  • ● Quiz4
• week5.Basics of dynamical systems
  • ● Part 15 Stability(1)
  • ● Part 15 Stability(2)
  • ● lecture_part 15
  • ● Part 16 Bifurcation(1)
  • ● Part 16 Bifurcation(2)
  • ● lecture_part 16
  • ● Part 17 Matrix analysis and linear algebra(1)
  • ● Part 17 Matrix analysis and linear algebra(2)
  • ● lecture_part 17
  • ● Quiz5
• week6.Firing rate model and networks
  • ● Part 18 Firing rate model， binary model and small networks(1)
  • ● Part 18 Firing rate model， binary model and small networks(2)
  • ● lecture_part 18
  • ● Part 19 Stability in two-dimensional systems(1)
  • ● Part 19 Stability in two-dimensional systems(2)
  • ● lecture_part 19
  • ● Part 20 Recurrent network-states and stability(1)
  • ● Part 20 Recurrent network-states and stability(2)
  • ● Part 20 Recurrent network-states and stability(3)
  • ● lecture_part 20
  • ● Quiz6
• week7.Memory and plasticity
  • ● Part 21 Memory(1)
  • ● Part 21 Memory(2)
  • ● Part 21 Memory(3)
  • ● Part 21 Memory(4)
  • ● lecture_part 21
  • ● Part 22 Oscillation
  • ● lecture_part 22
  • ● Part 23 Synaptic plasticity  The basics(1)
  • ● Part 23 Synaptic plasticity  The basics(2)
  • ● lecture_part 23
  • ● Quiz7
• week8.Learning
  • ● Part 24 Hebbian learning  The implementation(1)
  • ● Part 24 Hebbian learning  The implementation(2)
  • ● lecture_part 24
  • ● Part 25 Unsupervised learning(1)
  • ● Part 25 Unsupervised learning(2)
  • ● lecture_part 25
  • ● Part 26 Reinforcement learning  Classical conditioning(1)
  • ● Part 26 Reinforcement learning  Classical conditioning(2)
  • ● Part 26 Reinforcement learning  Classical conditioning(3)
  • ● Part 26 Reinforcement learning  Classical conditioning(4)
  • ● lecture_part 26
  • ● Quiz8
• week9.Operant conditioning and decision making
  • ● Part 27 Reinforcement learning  Operant conditioning(1)
  • ● Part 27 Reinforcement learning  Operant conditioning(2)
  • ● Part 27 Reinforcement learning  Operant conditioning(3)
  • ● lecture_part 27
  • ● Part 28 Decision making - Basic theory and Diffusion models
  • ● lecture_part 28
  • ● Part 29 Decision making  Attractor circuits and working memory
  • ● lecture_part 29
  • ● Part 30 Decision making  Winner-take-all dynamics and decision networks
  • ● lecture_part 30
  • ● Quiz9
• 計算神經科學課程總結
  • ● 計算神經科學課程總結

講師

羅中泉

國立清華大學生命科學系 教授 ｜ 查看講師

評價 (1)

清大生科學生

2021-07-31 13:17:18

受益良多，課程精心設計，羅老師的神經生物學也很推！！