Synaptic Dynamics:Synaptic Dynamics:Unsupervised Unsupervised

LearningLearning

Synaptic Dynamics:Synaptic Dynamics:Unsupervised Unsupervised

LearningLearningPart ⅠPart Ⅰ

Xiao BingXiao Bing

处理单元

处理单元

Input

Input

Output

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-

response pair ( , )i ix y

Six iy

• If is a sample from the function A system has learned if the system responses with for all ,and .

pn RRf →:( , )i ix y

fxy )(= xfy

Encoding

x′ y′

x

S

Close to Close to ,

y

• A system has partially learned or approximated the function .f

)(= xfy

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• 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.

Quantization1.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)

QuantizationProcess 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

nR k

im

imnR

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 axondendrit

e

axon

Neuron i Neuron j

Synapse

presynapticpostsynaptic

Input neuron

field

Output neuron

field

jim ,

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