2022-自學課程-計算神經科學(1-6月)

  • 0.0
  • 59 學生
  • 報名時間 : 2022/01/01 - 2022/06/30
  • 開課時間 : 2022/01/01 - 2022/06/30

報名課程

介紹

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,通過者可申請"修課證明"作為學習履歷之佐證。

“修課證明" 將於測驗完畢後,符合資格者系統計算分數(約半天)後立即發放。

章節

* 以下章節為預覽,請點報名後點選開始上課,進入課程
  • 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

常見問題

線上成績單 :
此課程不提供線上成績單

學習履歷 :
此課程不提供線上成績單


講師

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羅中泉

國立清華大學生命科學系 教授 | 查看講師

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2022-自學課程-計算神經科學(1-6月)

本課程中,將介紹神經細胞的基本構造與功能、神經訊號的產生與傳遞、神經細胞間的溝通與塑性、神經細胞的發育與改變、神經系統的退化與再生。藉由重要觀念的解說與實驗數據的分析,本課程期望所有修習的學生都能對神經細胞的特性有充分的瞭解,做為探索腦功能的重要基礎。