Laboratoire de physique statistique, Centre National de la Recherche Scientifique, Sorbonne University, University Paris-Diderot, École normale supérieure, PSL University, 75005 Paris, France.
Institut de la Vision, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Sorbonne University, 75012 Paris, France.
Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):3267-3272. doi: 10.1073/pnas.1718710115. Epub 2018 Mar 12.
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli.
大脑无法直接接触物理刺激，只能接触到感官器官中诱发的尖峰活动。目前尚不清楚大脑如何仅基于这些嘈杂的相关反应来学习刺激的表示。在这里，我们展示了如何仅从视网膜神经节细胞的群体活动结构构建准确的响应距离图。我们引入了时间受限玻尔兹曼机来学习群体活动的时空结构，并使用该模型定义尖峰序列之间的距离。我们表明，与几乎无法区分的刺激对相比，该度量标准在辨别方面优于现有的神经距离。所提出的方法为基于尖峰反应来学习关联相似刺激提供了一种通用且合理的生物学方法，而无需这些刺激的任何其他知识。
