Application and Computation of Probabilistic Neural Plasticity
This work addresses a gap in understanding neural plasticity probabilities, with potential applications in behavioral science, machine learning, AI, and psychiatry, but it appears incremental as it builds on existing mathematical models without claiming major breakthroughs.
The paper tackles the problem of quantifying the probability of neural plasticity occurring given an event, using ordinary differential equations, neural firing equations, and spike-train statistics to formulate an additive short-term memory equation for computing probabilistic neural plasticity.
The discovery of neural plasticity has proved that throughout the life of a human being, the brain reorganizes itself through forming new neural connections. The formation of new neural connections are achieved through the brain's effort to adapt to new environments or to changes in the existing environment. Despite the realization of neural plasticity, there is a lack of understanding the probability of neural plasticity occurring given some event. Using ordinary differential equations, neural firing equations and spike-train statistics, we show how an additive short-term memory (STM) equation can be formulated to approach the computation of neural plasticity. We then show how the additive STM equation can be used for probabilistic inference in computable neural plasticity, and the computation of probabilistic neural plasticity. We will also provide a brief introduction to the theory of probabilistic neural plasticity and conclude with showing how it can be applied to multiple disciplines such as behavioural science, machine learning, artificial intelligence and psychiatry.