The document discusses various types of Hebbian learning including:
1) Unsupervised Hebbian learning where weights are strengthened based on actual neural responses to stimuli without a target output.
2) Supervised Hebbian learning where weights are strengthened based on the desired neural response rather than the actual response to better approximate a target output.
3) Recognition networks like the instar rule which only updates weights when a neuron's output is active to recognize specific input patterns.
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
An artificial neuron network (neural network) is a computational model that mimics the way nerve cells work in the human brain. Artificial neural networks (ANNs) use learning algorithms that can independently make adjustments - or learn, in a sense - as they receive new input
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How libraries can support authors with open access requirements for UKRI fund...
Hebbian Learning
1.
2. ART1 Demo Increasing vigilance causes the network to be more selective, to introduce a new prototype when the fit is not good. Try different patterns
4. Hebb’s Postulate “ 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 or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” D. O. Hebb, 1949 A B In other words, when a weight contributes to firing a neuron, the weight is increased. (If the neuron doesn’t fire, then it is not).
16. Learning Banana Smell Initial Weights: Training Sequence: First Iteration (sight fails, smell present): = 1 unconditioned (shape) conditioned (smell) a 1 h a r d l i m w 0 p 0 1 w 0 p 1 0.5 – + h a r d l i m 1 0 0 1 0.5 – + 0 (no banana) = = =
17. Example Second Iteration (sight works, smell present): Third Iteration (sight fails, smell present): Banana will now be detected if either sensor works. a 2 h a r d l i m w 0 p 0 2 w 1 p 2 0.5 – + h a r d l i m 1 1 0 1 0.5 – + 1 (banana) = = =
18.
19. Hebb Rule with Decay This keeps the weight matrix from growing without bound, which can be demonstrated by setting both a i and p j to 1:
21. Example: Banana Associator First Iteration (sight fails, smell present): Second Iteration (sight works, smell present): = 0.1 = 1 a 1 h a r d l i m w 0 p 0 1 w 0 p 1 0.5 – + h a r d l i m 1 0 0 1 0.5 – + 0 (no banana) = = = a 2 h a r d l i m w 0 p 0 2 w 1 p 2 0.5 – + h a r d l i m 1 1 0 1 0.5 – + 1 (banana) = = =
24. Problem of Hebb with Decay Associations will be lost if stimuli are not occasionally presented. If a i = 0, then If = 0, this becomes Therefore the weight decays by 10% at each iteration where there is no stimulus.
27. Instar Operation The instar will be active when or For normalized vectors, the largest inner product occurs when the angle between the weight vector and the input vector is zero -- the input vector is equal to the weight vector. The rows of a weight matrix represent patterns to be recognized. w T 1 p w 1 p cos b – =
28. Vector Recognition If we set the instar will only be active when = 0. If we set the instar will be active for a range of angles. As b is increased, the more patterns there will be (over a wider range of ) which will activate the instar. b w 1 p – = b w 1 p – > w 1
29. Instar Rule Hebb with Decay Modify so that learning and forgetting will only occur when the neuron is active - Instar Rule: or Vector Form: w i j q w i j q 1 – a i q p j q a i q w q 1 – – + = i j
30. Graphical Representation For the case where the instar is active ( a i = 1): or For the case where the instar is inactive ( a i = 0):
33. Outstar Operation Suppose we want the outstar to recall a certain pattern a * whenever the input p = 1 is presented to the network. Let Then, when p = 1 and the pattern is correctly recalled. The columns of a weight matrix represent patterns to be recalled.
34. Outstar Rule For the instar rule we made the weight decay term of the Hebb rule proportional to the output of the network. For the outstar rule we make the weight decay term proportional to the input of the network. If we make the decay rate equal to the learning rate , Vector Form:
42. Hebb Rule Presynaptic Signal Postsynaptic Signal Simplified Form: Supervised Form: Matrix Form: actual output input pattern desired output
43. Batch Operation Matrix Form: (Zero Initial Weights) W t 1 t 2 t Q p 1 T p 2 T p Q T T P T = = T t 1 t 2 t Q = P p 1 p 2 p Q =
44. Performance Analysis Case I, input patterns are orthogonal. Therefore the network output equals the target: Case II, input patterns are normalized, but not orthogonal. Error term 0 q k =
45. Example Banana Apple Normalized Prototype Patterns Weight Matrix (Hebb Rule): Tests: Banana Apple
46. Pseudoinverse Rule - (1) Performance Index: Matrix Form: Mean-squared error T t 1 t 2 t Q = P p 1 p 2 p Q = || E || 2 e i j 2 j i =
47. Pseudoinverse Rule - (2) Minimize: If an inverse exists for P , F ( W ) can be made zero: When an inverse does not exist F ( W ) can be minimized using the pseudoinverse:
48. Relationship to the Hebb Rule Hebb Rule Pseudoinverse Rule If the prototype patterns are orthonormal: W T P T =