# Application and Computation of Probabilistic Neural Plasticity

## TL;DR

This paper introduces a mathematical framework using differential equations and spike-train statistics to compute and infer probabilistic neural plasticity, aiming to enhance understanding across neuroscience and AI.

## Contribution

It presents a novel approach to model and compute neural plasticity probabilistically using additive short-term memory equations and differential equations.

## Key findings

- Formulation of an additive STM equation for neural plasticity
- Application of probabilistic inference to neural plasticity modeling
- Potential interdisciplinary applications in AI, psychiatry, and behavioral science

## Abstract

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.