synaptic dynamics: unsupervised learning part Ⅰ xiao bing
TRANSCRIPT
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Synaptic Dynamics:Synaptic Dynamics:Unsupervised Unsupervised
LearningLearning
Synaptic Dynamics:Synaptic Dynamics:Unsupervised Unsupervised
LearningLearningPart ⅠPart Ⅰ
Xiao BingXiao Bing
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处理单元
处理单元
Input
Input
Output
Output
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outline• Learning• Supervised Learning and
Unsupervised Learning• Supervised Learning and
Unsupervised Learning in neural network
• Four Unsupervised Learning Laws
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outline• Learning• Supervised Learning and
Unsupervised Learning• Supervised Learning and
Unsupervised Learning in neural network
• Four Unsupervised Learning Laws
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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.
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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.
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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
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Encoding
x′ y′
x
S
Close to Close to ,
y
• A system has partially learned or approximated the function .f
)(= xfy
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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.
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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.
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Change
Please pay attention to:• We identify learning with change
in any synapse, not in a neuron.
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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.
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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.
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Quantization1.Learning replaces old stored patterns
with new patterns and forms “internal representations” or prototypes of sampled patterns.
2.Learned prototypes define quantized patterns.
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Quantization• Neural network models prototype patterns are presented
as vectors of real numbers. learning
“adaptive vector quantization” (AVQ)
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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
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outline• Learning• Supervised Learning and
Unsupervised Learning• Supervised Learning and
Unsupervised Learning in neural network
• Four Unsupervised Learning Laws
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Supervised Learning and Unsupervised
Learning
• Criterion Whether the learning algorithm
uses pattern-class information
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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
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outline• Learning• Supervised Learning and
Unsupervised Learning• Supervised Learning and
Unsupervised Learning in neural network
• Four Unsupervised Learning Laws
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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.
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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.
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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.
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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.
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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.
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outline• Learning• Supervised Learning and
Unsupervised Learning• Supervised Learning and
Unsupervised Learning in neural network
• Four Unsupervised Learning Laws
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Four Unsupervised Learning Laws
• Signal Hebbian• Competitive• Differential Hebbian• Differential competitive
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Four Unsupervised Learning Laws
dendrite axondendrit
e
axon
Neuron i Neuron j
Synapse
presynapticpostsynaptic
Input neuron
field
Output neuron
field
jim ,
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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.
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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.
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Differential Hebbian
• Correlating signal velocities as well as neuronal signals
• The signal velocity is obtained by differential of neuronal signal
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Differential competitive
• Combining competitive and differential Hebbian learning
• Learn only if change
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See also• Simple competitive learning applet
of neuronal networks http://www.psychology.mcmaster.ca/4i03/demos/competitive1-demo.html
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See also
• Kohonen SOM applet http://www.psychology.mcmaster.ca/4i03/demos/competitive-demo.html
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Welcome Wang Xiumei and Wang Ying to introduce four unsupervised learning laws in detail