Ganmor Elad, Segev Ronen, Schneidman Elad
Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel.
Department of Life Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Elife. 2015 Sep 8;4:e06134. doi: 10.7554/eLife.06134.
Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns.
信息在大脑中由大量嘈杂、不可靠的神经元的联合放电模式携带。这种噪声限制了神经编码的能力，并决定了信息如何被传输和读出。为了准确解码，大脑必须克服这种噪声，并识别哪些模式在语义上是相似的。我们使用网络编码噪声模型来学习脊椎动物视网膜中对人工和自然视频做出反应的神经元群体的同义词库，根据它们携带的信息来测量群体对视觉刺激的反应之间的相似性。这个同义词库揭示了编码是由同义活动模式的簇组织起来的，这些模式在意义上相似，但在结构上可能有很大差异。这种组织与工程编码的设计非常相似。我们认为大脑可能会利用这种结构，并展示它如何允许从新颖的放电模式中准确解码新颖的刺激。
