在视觉层次上对刺激特征的种群编码
Luciano Dyballa1, Andra M Rudzite2, Mahmood S Hoseini3
1Department of Computer Science, Yale University.
bioRxiv : the preprint server for biology
|July 10, 2023
概括
视网膜中的神经群体不间断地编码视觉特征,而主要视觉皮层 (V1) 显示了一个连续的表示. 这表明人工神经网络更像是视网膜而不是大脑.
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.
- 视觉科学 视觉科学 视觉科学
背景情况:
- 视网膜和主要视觉皮层 (V1) 包含对各种视觉特征敏感的多种神经群体.
- 了解这些神经群体如何划分刺激空间以表示特征至关重要,但仍然不清楚.
研究的目的:
- 为了调查视网膜和V1中的神经群体是否被组织成离散的群体或在特征编码空间中连续分布.
- 为了比较生物神经系统的特征分区策略与人工神经网络的特征分区策略.
主要方法:
- 呈现了一组视觉刺激对小鼠视网膜和V1.
- 使用多电极阵列测量神经反应.
- 开发了一种使用机器学习的多重嵌入技术,以分析神经群体反应及其与神经元性质的相关性.
主要成果:
- 发现视网膜种群可以谨慎地编码视觉特征.
- 在V1种群中,特征表现更加连续.
- 卷积神经网络 (CNN) 模拟视觉处理的分割特征类似于视网膜.
结论:
- 生物视觉处理在视网膜和V1.1之间表现出独特的特征分区策略.
- 在视觉处理模型中使用的CNN似乎更接近于模仿视网膜处理而不是皮质处理.
- 这表明当前的人工神经网络可能更类似于"大视网膜"而不是"小大脑".
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