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Dominic John Repici
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Influence Learning

 
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 (post-synaptic) neurons are learning (they are, after all, neurons).

By assuming that post-synaptic neurons are learning, pre-synaptic neurons are able to determine which forward neurons to make or strengthen connections with, based on how much influence they 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.




. . . . . . .
Neuron Body Values (Some)



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





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Resources

Also: OI_I     OI_E     Multitemporal Synapse

 
 


































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