Mechanisms of pattern decorrelation by recurrent neuronal circuits
Mechanisms of pattern decorrelation by recurrent neuronal circuits
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DOI：
10.1038/nn.2591
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发表时间：
2010-08-01
影响因子：
25
通讯作者：
Friedrich, Rainer W.
中科院分区：
文献类型：
--
作者：
Wiechert, Martin T.;Judkewitz, Benjamin;Friedrich, Rainer W.
去相关是优化神经元活动模式格式的基本计算。采用自适应机制的信道去相关可以提高编码效率，而模式去相关则有利于信息的读取和存储。然而，实现模式去相关的机制仍不清楚。我们开发了一个理论框架，以数学严格的方式将高维模式去相关与神经元和电路特性联系起来。对于一类一般的随机神经网络，我们证明了模式去相关产生于神经元非线性，并被循环连接放大。这种机制不需要网络的自适应，通过稀疏连接增强，依赖于基线膜电位，并且是鲁棒的。连通性测量和计算模型表明，这种机制涉及斑马鱼嗅球的模式去相关。这些结果揭示了神经元回路的结构和功能之间的一般关系，这可能与大脑不同区域的模式处理有关。
Decorrelation is a fundamental computation that optimizes the format of neuronal activity patterns. Channel decorrelation by adaptive mechanisms results in efficient coding, whereas pattern decorrelation facilitates the readout and storage of information. Mechanisms achieving pattern decorrelation, however, remain unclear. We developed a theoretical framework that relates high-dimensional pattern decorrelation to neuronal and circuit properties in a mathematically stringent fashion. For a generic class of random neuronal networks, we proved that pattern decorrelation emerges from neuronal nonlinearities and is amplified by recurrent connectivity. This mechanism does not require adaptation of the network, is enhanced by sparse connectivity, depends on the baseline membrane potential and is robust. Connectivity measurements and computational modeling suggest that this mechanism is involved in pattern decorrelation in the zebrafish olfactory bulb. These results reveal a generic relationship between the structure and function of neuronal circuits that is probably relevant for pattern processing in various brain areas.