循环皮层网络通过序列过编码自然感官统计数据.
Ciana E Deveau1,2,3, Zhishang Zhou1, Paul K LaFosse1,2,4
1Intramural Program, National Institute of Mental Health, National Institutes of Health; Bethesda, MD USA.
bioRxiv : the preprint server for biology
|June 21, 2024
概括
鼠标视觉皮层中的反复连接过输入序列,放大相关的输入序列. 这表明这些网络通过编码自然世界统计数据来执行预测处理.
科学领域:
- 神经科学是一个神经科学.
- 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
背景情况:
- 经常性神经网络 (RNN) 已知可以产生动态,但它们在感觉皮层处理中的作用尚不清楚.
- 在感官皮层内的动态处理中,密集的反复激发-激发网络的功能尚不清楚.
研究的目的:
- 研究老鼠视觉皮层在动态处理中的反复连接的作用.
- 为了确定经常性网络是否过或生成输入序列.
主要方法:
- 利用双光子光遗传学来测量小鼠视觉皮层中的神经反应.
- 呈现自然图像并重播它们以评估序列选择性.
- 设计特定的输入序列来测试网络放大和抑制机制.
主要成果:
- 视觉皮层中的反复连接通过过输入序列而不是生成它们来支持动态计算.
- 当在正确的动态环境中呈现时,神经对自然图像序列的反应会被放大,这与自然视觉相对应.
- 序列选择性是由一个网络机制介导的,早期的输入模式通过局部神经元相互作用影响了对后来的模式的反应.
结论:
- 视觉皮层中反复出现的皮层连接在根据动态环境过感官输入方面发挥着至关重要的作用.
- 这些网络表现出序列选择性,放大了与自然视觉的学习统计规律相匹配的输入.
- 这些发现表明,循环皮层网络参与预测处理,将环境统计编码到其输入-输出转换中.
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