Optimizing Peripheral Nerve Regeneration using Computational Intelligence based T

描述(申请人提供)：周围神经损伤是常见病，每年影响大量患者。组织工程学已经成为开发替代神经移植物用于周围神经再生的有效方法。由于周围神经再生中的组织工程策略涉及各种可能的变量组合，因此有必要开发有效的工具来确定最优的组织工程策略并基于这些组织工程策略预测周围神经再生的实验结果。一些研究小组已经应用人工神经网络和决策树来获得组织工程策略预测的最佳模型配置。对于基于决策树的方法，很难区分哪种分类树比另一种分类树好。此外，使用决策树算法的预测系统缺乏随时间积累学习经验的能力。另一方面，人工神经网络(ANN)表现出一些显著的性质，但只有连接权值是用固定的拓扑结构来训练的。对于每一种特定的组织工程策略，都很难预先找到最佳的固定拓扑。在该方案中，提出了基于群体智能(SI)的进化人工神经网络技术来应对这一挑战。本项目将采用两种基于群体智能的方法--蚁群算法(ACO)和粒子群算法(PSO)来训练神经网络模型。更具体地说，蚁群算法将用于优化神经网络模型的拓扑结构，而粒子群算法则用于根据优化后的拓扑结构调整神经网络模型的连接权。这种基于群智能的强化学习方法能够同时自动进化人工神经网络的拓扑结构和连接权重，从而为周围神经再生中的组织工程策略提供最优的分类器。研究项目将包括以下几个阶段：目标1：使用基于群智能的强化学习方法预测周围神经再生的组织工程策略(SWIRL-ANN)分析预测系统。目的：利用基于漩涡神经网络的分析预测系统，验证新型未知组织工程神经移植物在大鼠坐骨神经损伤模型中修复周围神经缺损区的有效性。 与公共健康相关：组织工程学已经成为开发神经移植物用于周围神经再生的有效方法。由于周围神经再生中的组织工程策略涉及各种可能的变量组合，因此有必要开发有效的工具来确定最优的组织工程策略并基于这些组织工程策略预测周围神经再生的实验结果。在该方案中，提出了基于群智能(SI)的进化人工神经网络(ANN)技术来应对这一挑战。这项研究将有助于有效地开发用于组织和器官替代的组织工程产品。

DESCRIPTION (provided by applicant): Peripheral nerve injuries are common diseases that affect a large amount of patients every year. Tissue engineering has emerged as a powerful approach for developing alternative nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. Some research groups have applied artificial neural networks and decision trees to obtain the best model configuration for the prediction of the tissue engineering strategies. For the decision trees based methods, it is hard to tell which classification tree is better than the other. Furthermore, the prediction system using the decision tree algorithm lacks the capability of accumulating the learning experience over time. On the other hand, Artificial Neural Networks (ANNs) exhibit some remarkable properties, but only the connection weights are trained with fixed topology. It is hard to find the best fixed topology in advance for each specific tissue engineering strategy. In this proposal, swarm intelligence (SI) based evolving ANNs technique is proposed to tackle this challenge. Two swarm intelligence based methods, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), will be applied in this project to train the ANN model. More specifically, ACO will be used to optimize the topology structure of the ANN models, while the PSO is used to adjust the connection weights of the ANN models based on the optimized topology structure. For this SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) system, both topology and connection weight of artificial neural networks can be evolved automatically and simultaneously so that an optimal classifier for tissue engineering strategies in peripheral nerve regeneration can be achieved. The research project will include the following phases: Aim 1: Predict tissue engineering strategies in peripheral nerve regeneration using SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) analytical and prediction system. Aim 2: Validate the efficacy of novel unknown tissue engineered nerve grafts as predicted by using SWIRL-ANN based analytical and prediction system for bridging peripheral nerve gaps in rat sciatic nerve injury model in vivo. PUBLIC HEALTH RELEVANCE: Tissue engineering has emerged as a powerful approach for developing nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. In this proposal, swarm intelligence (SI) based evolving artificial neural networks (ANNs) technique is proposed to tackle this challenge. The proposed research will be helpful to efficiently develop tissue engineered products for tissue and organ replacement.