- The error-correction learning rule may be implemented by using
inhibition to subtract the desired response (target value) from the
output, and then applying the anti-Hebbian rule. Discuss this
interpretation of error-correction learning.
- Figure 1 shows a two-dimensional set of data points. Part of the data
points belongs to class and the other part belongs to class
. Construct the decision boundary produced by the nearest
neighbor rule applied to this data sample.
- A generalized form of Hebb's rule is described by the relation

where and are the presynaptic and postsynaptic signals, respectively; and are functions of their respective arguments; and is the change produced in the synaptic weight at time in response to the signals and . Find the balance point and the maximum depression that are defined by this rule. - An input signal of unit amplitude is applied repeatedly to a
synaptic connection whose initial value is also unity. Calculate the
variation in the synaptic weight with time using the following rules:
- The simple form of Hebb's rule described by

assuming the learning rate . - The covariance rule described by

assuming that the time-averaged values of the presynaptic signal and postsynaptic signal are and , respectively.

- The simple form of Hebb's rule described by
- Formulate the expression for the output of neuron in
the network of Figure 2. You may use the following notations:
*i*th input signalsynaptic weight from input *i*to neuron*j*weight of lateral connection from neuron *k*to neuron*j*induced local field of neuron *j*

What is the condition that would have to be satisfied for neuron to be the winning neuron?**Figure 2:**Simple competitive learning network with feedforward connections from the source nodes to the neurons, and lateral connections among the neurons.

Jarkko Venna 2005-04-13