Effect of long-term potentiation on the behavior of neural networks

One of the characteristics of long-term potentiation (LTP) is that it decays as time passes, and the decay rates are different among different synapses. Many simulations have been done using different decay rates with randomized connections and LTPs. The faster the decay rate of the long-term potentiation, the more sensitive the network is to the stimuli. Therefore, the plasticity of the neural network increases when the long-term potentiation decays faster in a neural network. It is found that, by controlling the plasticity of a neural network, one can optimize the neural network connections. A very strong correlation is shown to exist between the duration of LTP and memory processing, perception, and cognition. Simulation also suggests that LTP can provide the basis for competitive learning and associative learning