public class MetaNetwork
extends Object
Meta-neurons and meta-synapses allow to generate new fully trained neurons based on a single training data set.
The meta-network employs an inhibitory feedback loop to determine if a certain information is already known (i.e.
there is already a neuron representing it) or if it is new knowledge that should be represented by a new neuron. If
there is already a neuron that represents this information, then the meta-neuron will be suppressed by the
feedback loop. Otherwise the meta-neuron will be activated which means a copy of the meta-neuron will be generated
using only the meta information of this neuron. The meta-synapses are only added to this new neuron if the input
neuron has beed active in the training data set as well. If the input neuron of the meta-synapse is another
inhibitory neuron, then the resulting synapse of the new neuron is going to be connected to the activated input
neuron of this inhibitory neuron.
- Author:
- Lukas Molzberger