在人体V1-V3中通过定向调整的周围环境进行正常化
Zeming Fang1,2, Ilona M Bloem1, Catherine Olsson1
1Department of Psychology and Center for Neural Science, New York University, New York City, New York, United States of America.
PLoS computational biology
|December 27, 2023
概括
一个新的视觉处理模型解释了大脑如何对直线和曲线有不同的反应. 这种"调整正常化"模型比较旧的模型更好地预测视觉皮层中的神经活动.
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.
- 视觉感知 视觉感知 视觉感知
背景情况:
- 正常化能量模型是一个突出的理论,解释了主要视觉皮层中的神经元反应.
- 这种模型涉及线性过,对比能量提取和复杂细胞的局部群体的分裂正常化.
研究的目的:
- 在解释视觉反应方面研究经典规范化能量模型的局限性.
- 提出和验证一个修改后的分割规范化模型,该模型可以解释对各种轮类型的差异反应.
主要方法:
- 利用人类功能磁共振成像 (fMRI) 来测量神经反应.
- 实施并测试了一种新的"调整正常化"模型,其中定向调整的细胞优先相互抑制.
- 对格和蛇的fMRI数据进行了验证,并将模型的预测与格和蛇的fMRI数据进行了验证,并将其概括为V1和外层皮层 (V2,V3) 的其他纹理.
主要成果:
- 经典的正常化能量模型未能预测观察到的fMRI反应,低估了对曲线轮 (蛇) 和直线轮 (格子) 的反应.
- 提出的"调整正常化"模型成功解释了网格和蛇之间的不同fMRI反应.
- 该模型在初级 (V1) 和外层 (V2,V3) 视觉皮层中展示了对其他带通纹理的概括.
结论:
- 经典的分裂性正常化模型不能完全捕捉到主要视觉皮层中的神经反应.
- 一个经过修改的"调整正常化"模型,结合了定向调整的特异性,更好地解释了神经处理.
- 像轮异质性这样的图像特征显著影响神经反应,即使在早期视觉区域.
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