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空间和时间的尺度不变神经表征的自监督学习。

Self-supervised learning of scale-invariant neural representations of space and time.

作者信息

Alipour Abolfazl, James Thomas W, Brown Joshua W, Tiganj Zoran

机构信息

Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA.

Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA.

出版信息

J Comput Neurosci. 2025 Mar;53(1):131-162. doi: 10.1007/s10827-024-00891-1. Epub 2025 Jan 22.

DOI:10.1007/s10827-024-00891-1
PMID:39841398
Abstract

Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning. We found that a network based on autoencoders can, given a particular inputs and connectivity constraints, produce scale-invariant time cells. When the animal's velocity modulates the decay rate of the leaky integrators, the same network gives rise to scale-invariant place cells. Importantly, this is not the case when velocity is fed as a direct input to the leaky integrators, implying that weight modulation by velocity might be critical for developing scale-invariant spatial receptive fields. Finally, we demonstrated that after training, scale-invariant place cells emerge in environments larger than those used during training. Taken together, these findings bring us closer to understanding the emergence of neurons with bell-shaped tuning curves in the hippocampus and highlight the critical role of velocity modulation in the formation of scale-invariant place cells.

摘要

海马体中对空间和时间的表征似乎共享一种共同的编码方案,其特征是具有被称为位置细胞和时间细胞的钟形调谐曲线的神经元。调谐曲线的特性与韦伯定律一致,即在没有视觉输入的情况下,时间细胞的宽度随峰值时间缩放,位置细胞的宽度随距离缩放。基于早期的计算工作,我们研究了具有此类特性的神经元如何通过自监督学习出现。我们发现,给定特定的输入和连接约束,基于自动编码器的网络可以产生尺度不变的时间细胞。当动物的速度调节泄漏积分器的衰减率时,同一个网络会产生尺度不变的位置细胞。重要的是,当速度作为直接输入馈入泄漏积分器时,情况并非如此,这意味着速度引起的权重调制可能对形成尺度不变的空间感受野至关重要。最后,我们证明在训练后,尺度不变的位置细胞会出现在比训练期间使用的环境更大的环境中。综上所述,这些发现使我们更接近于理解海马体中具有钟形调谐曲线的神经元的出现,并突出了速度调制在尺度不变位置细胞形成中的关键作用。

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