Modelling of neural plasticity for enhanced performance of brain-machine interfaces

神经可塑性建模以增强脑机接口的性能

• 批准号: DP140102947
• 负责人: Prof Anthony Burkitt
• 金额: $ 20.97万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140102947
• 关键词: Modelling; neural; plasticity; enhanced; performance

Plasticity of the brain is one of the great scientific challenges of neuroscience. The aim of this project is to model the synaptic changes that occur with reward-modulated spike-timing-dependent plasticity and apply the model to developing plasticity targeted brain-machine interfaces. The significance of this approach is that such plasticity targeted techniques provide the prospect of taking advantage of the underlying neural plasticity to optimise the form of the neural recording and electrical stimulation. The outcomes will be to greatly improve the performance of brain-machine interface in terms of measures such as the number and sensitivity of channels, as well as robustness and reliability.

大脑的可塑性是神经科学面临的最大科学挑战之一。该项目的目的是建立奖励调制尖峰时间依赖可塑性发生的突触变化模型，并将该模型应用于开发针对可塑性的脑机接口。这种方法的意义在于，这种可塑性靶向技术提供了利用潜在的神经可塑性来优化神经记录和电刺激的形式的前景。其结果将大大提高脑机接口在通道数量和灵敏度以及鲁棒性和可靠性等方面的性能。