Synaptic Dynamics: Unsupervised Learning Part Ⅰ Xiao Bing

处理 单元 Input 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 Change Quantization 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-response pair If is a sample from the function A system has learned if the system responses with for all ,and .

Encoding A system has partially learned or approximated the function . Close to Close to , S A system has partially learned or approximated the function .

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 calculus-exam-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 Quantization 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 from into regions of quantization or decision classes. Learned prototype vectors define synaptic points . If and only if some point moves in the pattern space ,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 Unsupervised learning Depending on the class membership of each training sample Using unlabelled pattern samples. More computational complexity 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. Using class-membership information to define a numerical error signal or vector guiding the estimated gradient descent The synapses don’t use the class membership of training samples.

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 Learning Laws dendrite axon Neuron i Neuron j Synapse presynaptic postsynaptic Input neuron field Output neuron field

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