Reading population codes: a neural implementation of ideal observers
Reading population codes: a neural implementation of ideal observers
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DOI：
10.1038/11205
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发表时间：
1999-08-01
影响因子：
25
通讯作者：
Pouget, A
中科院分区：
文献类型：
--
作者：
Deneve, S;Latham, PE;Pouget, A
许多感觉和运动变量在神经系统中被大量具有钟形调谐曲线的神经元的活动编码。由于神经元反应中固有的噪声，从这些种群编码中提取信息是困难的。在大多数感兴趣的情况下，最大似然（ML）是最好的读出方法，理想的观察者会使用它。通过模拟和分析，我们证明了在生物学上合理的皮层回路模型中可以实现与ML的近似。我们的结果适用于广泛的非线性激活函数，表明皮层区域通常可以作为前一个区域活动的理想观察者。
Many sensory and motor variables are encoded in the nervous system by the activities of large populations of neurons with bell-shaped tuning curves. Extracting information from these population codes is difficult because of the noise inherent in neuronal responses. In most cases of interest, maximum likelihood (ML) is the best read-out method and would be used by an ideal observer. Using simulations and analysis, we show that a close approximation to ML can be implemented in a biologically plausible model of cortical circuitry. Our results apply to a wide range of nonlinear activation functions, suggesting that cortical areas may, in general, function as ideal observers of activity in preceding areas.