整个大脑动态的现象学模型,使用带有功率合的神经振荡器网络
Anirban Bandyopadhyay1, Sayan Ghosh1, Dipayan Biswas1
1Indian Institue of Technology Madras, Biotechnology, Chennai, 600036, India.
Scientific reports
|October 7, 2023
概括
这项研究引入了一种新的可训练的振荡神经网络,用于模拟大脑动态. 该模型准确地模拟功能连接,并识别大脑网络,提供对结构损伤影响的见解.
科学领域:
- 计算神经科学是一种神经科学.
- 神经成像分析分析 神经成像分析
- 神经科学中的人工智能
背景情况:
- 大规模的大脑动态建模传统上在振荡网络中使用线性合.
- 了解结构性和功能性大脑连接之间的关系至关重要.
研究的目的:
- 为大规模的大脑动态建模提供一个通用,可训练的振荡神经网络.
- 研究结构损伤对大脑连接和恢复的影响.
主要方法:
- 一个两阶段模型,具有具有新型功率合的振荡层和复杂值的前层.
- 培训采用修改后的Hebbian学习来进行横向连接和复杂的向后传播,用于前阶段.
- 模拟休息状态大脑活动和功能连接.
主要成果:
- 该模型在模拟经验功能连接性 (相关系数为0.99) 中取得了高准确性.
- 成功识别了默认模式网络区域.
- 证明结构损失显著改变功能连接动态,可以在 silico 中恢复.
结论:
- 拟议的电源合振荡神经网络是建模大脑动态和功能连接的强大工具.
- 该模型为了解结构性脑损伤的影响和潜在的恢复策略提供了一个框架.
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