試聽影片

2021-自學課程-計算神經科學(7-12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。

2021-自學課程-計算神經科學(7-12月)

講師: 羅中泉

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。

  • 0.0
  • 296 個學生
  • 報名時間:
    2021/04/01 - 2022/01/31
  • 開課時間:
    2021/06/01 - 2021/12/31

  • 課程費用: 免費
    • 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。

    • * 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
    • teacher picture

      羅中泉


      國立清華大學生命科學系 教授


      相關敘述

      cclo@mx.nthu.edu.tw

      學經歷

      國立清華大學 系統神經科學研究所 教授兼所長

      波士頓大學物理博士

      研究專長

      計算神經科學、神經資訊學

    • 課程評價

      0
      課程評分
      0 %
      0 %
      0 %
      0 %
      0 %

      Reviews (0)

      • 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!!