【COMP305 LEC 8】

LEC 8 

Comp305 Part I. Artificial Neural Networks

Topic 3. Hebb’s Rules

1. Hebb’s Rules and the historical background

The McColloch-Pitts neuron made a base for a machine (network of

units) capable of

1. storing information and

2. producing logical and arithmetical operations on it

2. ANN learning rules

Definition:

ANN learning rule is the rule how to adjust the weights of connections to get desirable output .

Much work in Artificial Neural Networks focuses on the learning rules that define:

how to change the weights of connections between neurons to better adapt a network to serve some overall function .

3. Hebb’s Rule (1949)

a particular type of use-dependent modification of the connection strength of synapses

might underlie

learning in the nervous system.

In another word:

“… Cells that fire together, wire together …”

• The conditions that Hebb predicted would lead to changes in synaptic strength have now been found to cause the long-term potentiation in some neurons of hippocampus and other brain areas.

Hebb预测的会导致突触强度变化的条件现在已经被发现会导致海马体和其他大脑区域的一些神经元的长期增强。

The second equation emphasizes the correlation nature of a Hebbian synapse .

Sometimes the Hebb’s rule is referred to as activity product rule .

  Hebb’s original learning rule referred exclusively to excitatory synapses

第二个方程强调了赫比边突触的相关性。•有时Hebb规则被称为活动产品规则。•Hebb最初的学习规则只指兴奋性突触