Brief Overview of Connectionism to understand Learning Walter Schneider P2476 Cognitive Neuroscience of Human Learning & Instruction http://schneider.lrdc.pitt.edu/P2476/inde x.htm Slides adapted from U. Oxford Connectionist Summer School 1998 http://hincapie.psych.purdue.edu/CSS/ind ex.html Hinton Lectures on connectionism http://www.cs.toronto.edu/~hinton/csc321 /index.html David Plaut
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Brief Overview of Connectionism to understand Learning
Walter Schneider P2476 Cognitive Neuroscience of Human Learning
Specific Example NetTalk • NetTalk: Sejnowski, T. J. & Rosenberg, C. R. (1987) Parallel Networks that Learn to
Pronounce English Text Complex Systems 1 145-168
Learning input phonetic transcription of a child continuous speech
Simple Units
Learning Rules Change Connection Weights
• Learning rules calculate the difference between desired output and the correct output and use that difference to change weights to reduce the error.
Learning or 50,000 trials.
Note if assume 200 words per our (welfare household) and 5 hr/day, 1000/day or 50 days.
NetTalk DownloadInitial 0:46 20secLearn space 0:2:17 20sAfter 10K ep 3:50 20sTransfer 5:19 20s http://www.cnl.salk.edu/ParallelNetsPronounce/index.phpTransfer to new words same speaker 78%.
Graceful Deterioration and robust processing with fast relearning
More Hidden units better performance but slower learning
Unit Coding Unclear in Distributed Code
Hierarchical Clustering Sensible groupings
Performance characteristics
• With 120 hidden units– 98% within trained units– 75% generalization on dictionary of 20,012 words– 85% first pass and 90% and 97.5% after 55 passes.
• Adding 2 hidden layers of 80 units slightly improved generalization (but slows learning) – 97% after 55 passes, 80% generalization,
Summary Supervised LearningNetTalk – example of back propagation learning • Performed computation with simple units, connection weight
matrices, parallel activation• Learning rule provided error signal from supervisor to change
connection weights • It took man 105 trials to reach good performance going through
babbling to word production• Learning speed and generalization varied with nature of number of
units and levels• Showed good generalization to related words• Developed similarity space consistent with human clustering data• Performance was robust to loss of units and connection noise• Needed expert teacher with ability to reach in brain to set correct
states
How is this like and not like human learning?
• Similar– Lots of trials– Babbling for a while before it makes sense– Ability to learn any language (e.g., Dutch)– Generalization to new words– Creates similarity spaces
• Dissimilar – Teacher shows exact correctness by activating the correct output units– Use DecTalk only allowing correct simple output– Very simple network, small number of units– Sequential presentation of target– Learning reading not babbling/speech– Accuracy does not reach human level– Unlikely to be biologically implement able (high precision connections, back
propagate precision across levels– Does not learn from instruction but only experience
An input pattern is transformed to an output pattern.
• Activation States are Vectors
Each pattern of activity can be considered a unique point in a space of states. The activation vector identifies this point in space.
inv
x
y
zx
y
z
• Mapping Functions
T = F (S)
The network maps a source space S (the network inputs) to a target space T (the outputs).
The mapping function F is most likely complex. No simple mathematical formula can capture it explicitly.
• Hyperspace
Input states generally have a high dimensionality. Most network states are therefore considered to populate HyperSpace.
S T
outvinv
The Principle of SuperpositionMatrix 1
+1 -1 -1 +1
-0.25 +0.25 +0.25 -0.25 -1
-0.25 +025 +0.25 -0.25 -1
+0.25 -0.25 -0.25 +0.25 +1
+0.25 -0.25 -0.25 +0.25 +1
-1 +1 -1 +1
+0.25 -0.25 +0.25 -0.25 -1
-0.25 +0.25 -0.25 +0.25 +1
-0.25 +0.25 -0.25 +0.25 +1
+0.25 -0.25 +0.25 -0.25 -1
Matrix 2
0.0 0.0 +0.5 -0.5
-0.5 +0.5 0.0 0.0
0.0 0.0 -0.5 +0.5
+0.5 -0.5 0.0 0.9
Composite Matrix
Hebbian Learning
• Cellular Association“When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process of metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” (Hebb 1949, p.50)
• Learning Connections
Take the product of the excitation of the two cells and change the value of the connection in proportion to this product.
outinaaw
wain aout
• The Learning Rule
ε is the learning rate.
• Changing ConnectionsIf ain = 0.5, aout = 0.75, and ε = 0.5then Δw = 0.5(0.75)(0.5) = 0.1875
And if wstart = 0.0, then wnext = 0.1875
• Calculating CorrelationsInput Output
0 1 2
+ + +
+ - -
- + -
- - +
0 1
2
Models of English past tense• PDP accounts
– Single homogeneous architecture
– Superposition– Competition between
different different verb types result in overregularisation and irregularisation
Number of patterns limited by dimensionality of network.
• Input patterns must be orthogonal to each other
• Similarity effects.
outout at
• Perceptron Convergence Rule
Learning in a single weight network
Assume a teacher signal tout
Adaptation of Connection and Threshold (Rosenblatt 1958)
Note that threshold always changes if incorrect output.
Blame is apportioned to a connection in proportion to the activity of the input line.
x
y
z
inv
Input Neurons
Output Neuronsw
ain aout
ininoutout aaatw
Using an Error Signal
• Perceptron Convergence Rule“The perceptron convergence rule guarantees to find a solution to a mapping problem, provided a solution exists.” (Minsky & Papert 1969)
• An Example of Perceptron LearningBoolean Or
Training the network
Input Output
0 0 0
1 0 1
0 1 1
1 1 1
aout
w20 w21
In Out W20 W21 θ aout δ Δθ Δw
0 0 0 0.2 0.1 1.0 0 0 0 0
1 0 1 0.2 0.1 1.0 0 1.0 -0.5 0.5
0 1 1 0.7 0.1 0.5 0 1.0 -0.5 0.5
1 1 1 0.7 0.6 0.0 1 0 0 0
Gradient Descent• Least Mean Square Error (LMS)
Define the error measure as the square of the discrepancy between the actual output and the desired output. (Widrow-Hoff 1960)
•Plot an error curve for a single weight network
• Make weight adjustments by performing gradient descent – always move down the slope.
• Calculating the Error Signal
Note that Perceptron Convergence and LMS use similar learning algorithms – the Delta Rule
• Error Landscapes
Gradient descent algorithms adapt by moving downhill in a multi-dimensional landscape – the error surface.
Ball bearing analogy.
In a smooth landscape, the bottom will always be reached. However, bottom may not correspond to zero error.
p
atE 2outout
Weight Value
Err
or
dw
dEkw
in2
inout awatdw
dw
Past Tense Revisited
• Vocabulary Discontinuity– Up to 10 epochs – 8 irregulars + 2
– Protracted period of overregularisation but at low rates (typically < 5%).
– High frequency irregulars are robust to
overregularisation.
Simulated Performance on Irregular VerbsMarcus et al Scoring
0
10
20
30
40
50
60
70
80
90
100
20 120 220 320% I
rreg
Pas
t Te
nse
Cor
rect
Vocabulary Size
Using an Error Signal• Orthogonality Constraint
Number of patterns limited by dimensionality of network.
• Input patterns must be orthogonal to each other
• Similarity effects.
outout at
• Perceptron Convergence Rule
Learning in a single weight network
Assume a teacher signal tout
Adaptation of Connection and Threshold (Rosenblatt 1958)
Note that threshold always changes if incorrect output.
Blame is apportioned to a connection in proportion to the activity of the input line.
x
y
z
inv
Input Neurons
Output Neuronsw
ain aout
ininoutout aaatw
Using an Error Signal
• Perceptron Convergence Rule“The perceptron convergence rule guarantees to find a solution to a mapping problem, provided a solution exists.” (Minsky & Papert 1969)
• An Example of Perceptron LearningBoolean Or
Training the network
Input Output
0 0 0
1 0 1
0 1 1
1 1 1
aout
w20 w21
In Out W20 W21 θ aout δ Δθ Δw
0 0 0 0.2 0.1 1.0 0 0 0 0
1 0 1 0.2 0.1 1.0 0 1.0 -0.5 0.5
0 1 1 0.7 0.1 0.5 0 1.0 -0.5 0.5
1 1 1 0.7 0.6 0.0 1 0 0 0
Gradient Descent• Least Mean Square Error (LMS)
Define the error measure as the square of the discrepancy between the actual output and the desired output. (Widrow-Hoff 1960)
•Plot an error curve for a single weight network
• Make weight adjustments by performing gradient descent – always move down the slope.
• Calculating the Error Signal
Note that Perceptron Convergence and LMS use similar learning algorithms – the Delta Rule
• Error Landscapes
Gradient descent algorithms adapt by moving downhill in a multi-dimensional landscape – the error surface.
Ball bearing analogy.
In a smooth landscape, the bottom will always be reached. However, bottom may not correspond to zero error.
p
atE 2outout
Weight Value
Err
or
dw
dEkw
in2
inout awatdw
dw
Past Tense Revisited
• Vocabulary Discontinuity– Up to 10 epochs – 8 irregulars + 2