依赖于尖峰时间的可塑性部分补偿了运动敏感神经元多层网络中神经延迟
Charlie M Sexton1, Anthony N Burkitt2,3, Hinze Hogendoorn1,4
1Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia.
PLoS computational biology
|September 6, 2023
概括
大脑通过推断移动物体位置来弥补神经延迟. 在多层网络中,峰值定时依赖可塑性 (STDP) 减少了这种表示滞后,改善了实时视觉处理.
科学领域:
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
- 视觉处理 视觉处理
背景情况:
- 神经处理时间导致代表性滞后,其中大脑的表示是过时的.
- 这种延迟对于准确地定位视野中的移动物体至关重要.
- 大脑可以通过推断物体运动来弥补延迟.
研究的目的:
- 为了研究如何尖峰时间依赖的可塑性 (STDP) 可以补偿多层视觉网络中神经延迟.
- 以生物学上可信的方式建模运动推断并减少表示滞后.
- 为了检查STDP驱动的受体场转移在多个层中的积累.
主要方法:
- 开发了一种具有速度调节神经元的多层前神经网络.
- 实现了现实的突触时间流程和尖峰时间依赖的可塑性 (STDP).
- 模拟了刺激运动,并分析了受感场移动和表示滞后.
主要成果:
- STDP诱导受体场的变化,使它们与刺激运动相反.
- 在各层之间以速度依赖的方式减少了表示滞后.
- 多层网络通过前路径证明了累积的延迟补偿.
结论:
- 峰值定时依赖可塑性 (STDP) 是补偿视觉处理中神经延迟的一个可行的机制.
- 通过STDP的前通路在开发运动外推能力方面发挥着关键作用.
- 这种以生物学为灵感的模型突出了克服代表性差距的发展战略.
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