预测错误的电路级建模,在自愿行动中计算多维特征的预测错误
1School of Mathematical Science, Zhejiang University, Hangzhou, China.
Frontiers in computational neuroscience
|October 15, 2025
概括
这项研究模拟了大脑如何使用预测处理来抑制自愿行动期间自我生成的感官输入. 计算模型显示经验依赖的抑制和平衡的神经通路是这种感官抑制机制的关键.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 神经生物学 神经生物学 神经生物学
背景情况:
- 预测处理理论通过尽量减少预测错误来解释感知,认知和行动.
- 在自愿行动中,大脑被认为可以抑制自我产生的感觉结果.
- 对于神经计算在自愿行动期间的预测处理中存在有限的机制理解.
研究的目的:
- 开发一个神经回路的计算模型,用于在自愿行动中进行预测处理.
- 为了研究大脑如何积极抑制自我生成的感官反.
- 探索抑制性可塑性和激发-抑制平衡在这个过程中的作用.
主要方法:
- 开发了一个使用金字塔细胞和抑制性内神经元的计算模型,结合了生物现实的连接性.
- 包括经验依赖的抑制可塑性和特征选择性来塑造激发-抑制平衡.
- 将模型扩展到一个二维的预测错误电路,其中有分离的,上向下调制的树突,以实现组合选择性.
主要成果:
- 上下预测选择性地抑制金字塔细胞,通过经验依赖的抑制性可塑性通过匹配的特征选择性.
- 抑制依赖于抑制性内部神经元反应的选择性和跨通路的平衡激发抑制.
- 该模型成功地适应了涉及两个独立特征的预测.
结论:
- 该研究提供了对神经回路的计算洞察力,用于自愿行动中的主动感官抑制.
- 这些发现突出了抑制性可塑性和E/I平衡在预测编码中的相互作用.
- 这项工作促进了对大脑如何产生预测以指导行为的理解.
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