A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network

you may have heard that the Brain is plastic. As you know the brain is not made of plastic, Brain Plasticity also called Neuroplasticity. Brain plasticity is a physical process. Gray matter can actually shrink or thicken neural connections can be forged and refined or weakened and severed. Brain Plasticity refers to the brain’s ability to change throughout life. The brain has the amazing ability to reorganize itself by forming new connections among brain cells (neurons). For a long time, it was believed that as we aged, the connections in the brain became fixed. Research has shown that in fact the brain never stops changing through learning. Plasticity is the capacity of the brain to change with learning. Changes associated with learning occur mostly at the level of the connections among neurons. New connections can form and the internal structure of the existing synapses can change but also partially its internal topology, according to either the received external stimuli and the pre existent connection. We have found this idea can be applied also to the simple Artificial Neural Network. In this paper we have proposed a new method is presented to adapt dynamically the topology of an Artificial Neural Network using only the information on learning set. And also in this paper we have proposed algorithm has been tested on result relative to the Multilayer Perceptron (MLP) problem. Index Terms — Learning, Neuroplasticity, Multilayer Perceptron (MLP), Artificial Neural Network (ANN), Neuron, Brain, Synaptic. ——————————  ——————————