Also Influence Based Learning - is a new learning method, or learning algorithm, that has been discovered as part of the Netlab development effort. It is based on the idea that pre-synaptic neurons can trust that forward neurons are learning (they are, after all, neurons). By assuming that they are learning, pre-synaptic neurons are able to determine which forward neurons to make or strengthen connections with, based on how much influence those forward neurons are exercising over current responses. This is, in fact, how and why influence learning works.

Because influence over current responses is determined during signal propagation, there is no need to calculate an error value at each output neuron and propagate it backwards through the network. This, in turn, eliminates the need to restrict networks to layered (feed-forward-only) topologies.

Influence learning is a Netlab term with no direct analog in the study of biological neural networks. It is, however, loosely based on known biological observations.



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Resources

• Introducing Influence Learning
• Biological Underpinnings of Influence Learning
• Influence Learning Gets A Patent
  

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Neuron Body Values (Some)

NBValue	Description
II	Input Influenced
II_E	Input Influenced from Excitatory Inputs
II_I	Input Influenced from Inhibitory Inputs
OI	Output Influenced
OI_E	Output Influenced from Excitatory Inputs
OI_I	Output Influenced from Inhibitory Inputs