Pan Maturing Process and Physiology
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PHYSIOLOGY: PLASTICITY - A METAPHYSIC APPROACH
The beauty and problem of this brain wiring are; be able to re-wire, reroute, present great degree of plasticity property in a very short time…,
In the most general sense, brain “plasticity” refers to changes in the wiring, or connectivity, of neurons. This was originally studied as the response to damage, such as a stroke or impact injury, but has progressed to mean just about any change in neurons in number and the interconnections between them, especially at the synapse level. If you think about it, every change in our behavior that involves learning and memory, including motor memory, have to involve some sort of molecular, physiological, or anatomical change in the brain. Further, having thought about it, you have made a change in your brain.
I think the proper neuroscientific term is Neuroplasticity.
IMPORTANCE OF METAPLASTICITY
Metaplasticity helps to prevent synapses from reaching a point of saturation or extinction, by maintaining synapses within a dynamic range of plasticity. The information storage capacity of a neuronal network would be severely limited in the absence of metaplasticity, because synapses would quickly reach an upper or lower limit of plasticity. Consider the example of a learning experience where a subset of synapses become strongly potentiated. Because these synapses are strengthened, they become more likely to drive postsynaptic firing of action potentials and to potentiate again with subsequent neural activity. These synapses would eventually reach a point of saturation if their ability to potentiate were left unchecked. Metaplasticity acts to reset the plasticity threshold to help prevent this saturation, and therefore maintains synapses within a dynamic range capable of both potentiating and weakening. Thus, metaplasticity endows synapses and networks with an ongoing ability to respond to an ever-changing environment. This allows neural networks to develop properly in an experience-driven manner and provides neural networks with a continued capacity to learn.