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駆け足で読む『Bio-Inspired Artificial Intelligence』の中身　3. 神経学の系

人工知能 数理生物学 教科書 駆け足で読むシリーズ
  • 素子があって、相互作用のルールがあって、構造を作って、構造が機能を支える仕組み
  • 3. Neural Systems
    • Computational neuroscience 計算機を用いた神経科学
    • Neural engineering 機械制御
    • Baldwin effect?
    • 3.1 Biological Nervous Systems
      • Dendrites, axon, synapses, neurotransmitters, activation level,action potential, spike
      • 3.1.1 Neural typology 神経分類学
        • Excitatory, inhibitory
        • Sensory, mortor
        • Interneurons
      • 3.1.2 Neural Communications 神経の連絡
        • Firing rate, firing time, direct connections, long-range neurotransitters
      • 3.1.3 Neural Topology 神経の形態学
        • Canonical circuit 基本回路
        • Adaptations
        • Hebb's rule 繰り返しで強化される
        • Long-term potentiation, Long-term depression
        • Spike time-dependent plasticity
    • 3.2 Artificial Neural Networks
      • Robustness, flexibility, generalization, content-based tetrieval
    • 3.3 Neuron Models ニューロンのモデル
      • McCulloch and Pitts model
      • Sigmoid function
      • Separation line
      • Lineary separable
      • Discrete-time/continuous-time recurrent neural network
      • Leaky integrator
      • Spiking neurons
      • Integrate and fire nueron
      • Spike response model
    • 3.4 Architecture
      • Feedforward
      • Recurrent connections
      • Autoassociative networks
      • Echo state networks
      • Liquid state machines
    • 3.5 Signal Encoding
      • Local encoding, distributed encoding, receptive field, normalization, encoding with spikes
    • 3.6 Synaptic Plasticity
      • Unsupervised learning, supervised learning
      • Hebb's rule
      • Spike time-dependent plasticity
      • Training phase, testing phase
    • 3.7 Unsupervised Learning
      • 3.7.1 Feature detection
      • 3.7.2 Multilayered Feature detection
      • 3.7.3 Self-organizing maps
      • 3.7.4 Adaptive Resonance Theory
        • Plasticity-stability dilemma
        • Adaptive resonance theory
        • Initialization phase, recognition phaes, comparison phase, research phase, adaptation phase
    • 3.8 Supervised Learning
      • 3.8.1 Backpropagation of error エラーの逆伝搬
      • 3.8.2 Using Backpropagation
      • 3.8.3 Sample Applications of Backpropagation
    • 3.9 Reinforcement Learning
    • 3.10 Evolution of Neural Networks
    • 3.11 Neural Hardware
    • 3.12 Hybrid Neural Systems
    • 3.13 Closing Remarks
    • 3.14 Suggested Readings