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首页> 外文期刊>PLoS Computational Biology >Temporal pattern separation in hippocampal neurons through multiplexed neural codes
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Temporal pattern separation in hippocampal neurons through multiplexed neural codes

机译:海马神经元的时空模式分离

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Author summary Pattern separation (the process of disambiguating incoming patterns of neuronal activity) is a central concept in all current theories of episodic memory, as it is hypothesized to support our ability to avoid confusion between similar memories. For the last thirty years, pattern separation has been attributed to the dentate gyrus of the hippocampus, but this has been hard to test experimentally. Moreover, because it is unclear how to define activity patterns in the brain, such a computation could be achieved in many different ways. Here, we demonstrate that pattern separation is performed by hippocampal networks (dentate gyrus and CA3) through a variety of neural codes. By systematically testing different definitions of what it means for spike trains to be similar (using a range of time scales and both standard and innovative metrics that assume different views of the neural code), we assessed how the input-output transformation of multiple hippocampal celltypes relate to pattern separation and found that different celltypes favor complementary coding strategies. This might help storing rich but concise and unambiguous representations of complex events. Finally, we provide the first experimental evidence of the importance of inhibitory signals in mediating pattern separation, and identify through which coding strategies.
机译:作者摘要模式分离(消除神经元活动的传入模式歧义的过程)是当前所有情景记忆理论中的核心概念,因为它被假设为支持我们避免相似记忆之间混淆的能力。在过去的三十年中,模式分离一直归因于海马的齿状回,但是这很难通过实验进行测试。此外,由于尚不清楚如何定义大脑的活动模式,因此可以通过许多不同的方式来实现这种计算。在这里,我们证明了模式分离是由海马网络(齿状回和CA3)通过各种神经代码执行的。通过系统地测试相似的峰值训练含义的不同定义(使用一定范围的时间标度以及采用不同假设的神经代码的标准和创新指标),我们评估了多种海马细胞类型的输入-输出转换与模式分离有关,发现不同的细胞类型有利于互补编码策略。这可能有助于存储复杂事件的丰富但简洁明了的表示形式。最后,我们提供了抑制信号在介导模式分离中的重要性的第一个实验证据,并确定了通过哪种编码策略。

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