人工神经网络ANN.ppt

4wk.baidu.com
Biological Neural Networks
10/10/2020
• 10,000 synapses per neuron
• Computational power = connectivity
• Plasticity
– new connections (?)
– strength of connections modified
• 10 billion neurons in human brain • Summation of input stimuli
– Spatial (signals) – Temporal (pulses)
• Threshold over composed inputs • Constant firing strength
Artificial Neural Networks - I
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Neural Dynamics
40 mV 20
membrane rest activation
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Action potential
-20
-40 -60 -80 -100 -120
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Refractory time
Action potential ≈ 100mV Activation threshold ≈ 20-30mV Rest potential ≈ -65mV Spike time ≈ 1-2ms Refractory time ≈ 10-20ms
wi2
wi3
wi4
j wij xj (t)
yi f (ui (t))
wi5
Neuron i
urest = resting potential xj(t) = output of neuron j at time t wij = connection strength between neuron i and neuron j u(t) = total stimulus at time t
• Unsupervised ANNs – Examples – Applications – Further topics
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Artificial Neural Networks - I
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Contents - I
• Introduction to ANNs
– Processing elements (neurons) – Architecture
• 106 billion synapses in human brain
• Chemical transmission and modulation of signals
• Inhibitory synapses • Excitatory synapses
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Artificial Neural Networks - I
Artificial Neural Networks 人工神经网络
Introduction
Table of Contents
• Introduction to ANNs – Taxonomy – Features – Learning – Applications
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• Supervised ANNs – Examples – Applications – Further topics
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Artificial Neural Networks - I
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神经网络的复杂性
• 神经网路的复杂多样，不仅在于神经元和突触 的数量大、组合方式复杂和联系广泛，还在于 突触传递的机制复杂。现在已经发现和阐明的 突触传递机制有：突触后兴奋，突触后抑制， 突触前抑制，突触前兴奋，以及“远程”抑制 等等。在突触传递机制中，释放神经递质是实 现突触传递机能的中心环节，而不同的神经递 质有着不同的作用性质和特点
• Functional Taxonomy of ANNs • Structural Taxonomy of ANNs • Features • Learning Paradigms • Applications
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Artificial Neural Networks - I
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The Biological Neuron
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Artificial Neural Networks - I
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神经网络的研究
• 神经系统活动，不论是感觉、运动，还是脑的 高级功能（如学习、记忆、情绪等）都有整体 上的表现，面对这种表现的神经基础和机理的 分析不可避免地会涉及各种层次。这些不同层 次的研究互相启示，互相推动。在低层次（细 胞、分子水平）上的工作为较高层次的观察提 供分析的基础，而较高层次的观察又有助于引 导低层次工作的方向和体现其功能意义。既有 物理的、化学的、生理的、心理的分门别类研 究，又有综合研究。
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Artificial Neural Networks - I
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The Artificial Neuron
Stimulus
ui t wij x j t
j
Response
yi t f urest ui t
x1(t) x2(t) x3(t) x4(t) x5(t)
wi1
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Artificial Neural Networks - I
yi(t)
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Artificial Neural Models
• McCulloch Pitts-type Neurons (static)
– Digital neurons: activation state interpretation (snapshot of the system each time a unit fires)
– Analog neurons: firing rate interpretation (activation of units equal to firing rate)
– Activation of neurons encodes information
• Spiking Neurons (dynamic)