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Synaptic Dynamics: Unsupervised Learning Part Ⅰ Xiao Bing Input Output 处理 单元 Input 处理 单元 Output outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws Learning • Encoding A system learns a pattern if the system encodes the pattern in its structure. • Change A system learns or adapts or “self -organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment, so quantization is necessary. Learning • Encoding: A system learns a pattern if the system encodes the pattern in its structure. • • Change: A system learns or adapts or “self -organizes” when sample data changes system parameters. Quantization A system learns only a small proportion of all patterns in the sampled pattern environment . Encoding • A system has Learned a stimulus( xi , yi )pair response xi S yi • If ( xi , yi ) is a sample from the function f : Rn → R p A system has learned f if the system y = f ( x) responses with y for x all ,and . Encoding Close to x Close to x′ S y′ • A system has partially learned or approximated f the function . y , y = f ( x) Learning • Encoding: A system learns a pattern if the system encodes the pattern in its structure. • Change: A system learns or adapts or “self organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment. Change • We have learned calculus if our calculusexam-behavior has changed from failing to passing. • A system learns when pattern stimulation change a memory medium and leaves it changed for some comparatively long stretch of time. Change Please pay attention to: • We identify learning with change in any synapse, not in a neuron. Learning • • Encoding: A system learns a pattern if the system encodes the pattern in its structure. Change: A system learns or adapts or “self -organizes” when sample data changes system parameters. • Quantization A system learns only a small proportion of all patterns in the sampled pattern environment. Quantization Pattern space sampling Sampled pattern space quantizing Quantized pattern space Uniform(一致的) sampling probability provides an information-theoretic criterion for an optimal quantization. Quantization 1.Learning replaces old stored patterns with new patterns and forms “internal representations” or prototypes of sampled patterns. 2.Learned prototypes define quantized patterns. Quantization • Neural network models prototype patterns are presented as vectors of real numbers. learning “adaptive vector quantization” (AVQ) Quantization Process of learning • Quantize pattern space fromR n into k regions of quantization or decision classes. • Learned prototype vectors define synaptic points mi . • If and only if some pointmi moves in the pattern space R n ,the system learns see also figure 4.1, page 113 outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws Supervised Learning and Unsupervised Learning • Criterion Whether the learning algorithm uses pattern-class information Supervised learning Depending on the class membership of each training sample More computational complexity Unsupervised learning Using unlabelled pattern samples. Less computational complexity More accuracy Less accuracy allowing algorithms to detect pattern misclassification to reinforce the learning process Be practical in many high-speed real time environments outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws Supervised Learning and Unsupervised Learning in neural network • Besides differences presented before, there are more differences between supervised learning and unsupervised learning in neural network. Supervised learning Unsupervised learning Referring to estimated gradient descent in the space of all possible synaptic-value combinations. Referring to how biological synapses modify their parameters with physically local information about neuronal signals. The synapses don’t use the class membership of training samples. Using class-membership information to define a numerical error signal or vector guiding the estimated gradient descent Unsupervised Learning in neural network • Local information is information physically available to the synapse. • The differential equations define unsupervised learning laws and describe how synapses evolve with local information. Unsupervised Learning in neural network • Local information include: synaptic properties or neuronal signal properties information of structural and chemical alterations in neurons and synapses …… Synapse has access to this information only briefly. Unsupervised Learning in neural network Function of local information • Allowing asynchronous synapses to learn in real time. • Shrinking the function space of feasible unsupervised learning laws. outline • Learning • Supervised Learning and Unsupervised Learning • Supervised Learning and Unsupervised Learning in neural network • Four Unsupervised Learning Laws Four Unsupervised Learning Laws • • • • Signal Hebbian Competitive Differential Hebbian Differential competitive Four Unsupervised Input neuron Learning Laws field Neuron i presynaptic dendrite axon Synapse Neuron j Output neuron field postsynaptic dendrit e axon mi , j Signal Hebbian • Correlating local neuronal signals • If neuron i and neuron j are activated synchronously, energy of synapse is strengthened, or energy of synapse is weakened. Competitive • Modulating the signal-synaptic difference with the zero-one competitive signal (signal of neuron j ). • Synapse learns only if their postsynaptic neurons win. • Postsynaptic neurons code for presynaptic signal patterns. Differential Hebbian • Correlating signal velocities as well as neuronal signals • The signal velocity is obtained by differential of neuronal signal Differential competitive • Combining competitive and differential Hebbian learning • Learn only if change See also • Simple competitive learning applet of neuronal networks http://www.psychology.mcmaster.ca/ 4i03/demos/competitive1-demo.html See also • Kohonen SOM applet http://www.psychology.mcmaster.ca/ 4i03/demos/competitive-demo.html Welcome Wang Xiumei and Wang Ying to introduce four unsupervised learning laws in detail