A neurally efficient implementation of sensory population decoding.
A neurally efficient implementation of sensory population decoding.
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DOI：
10.1523/jneurosci.6776-10.2011
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发表时间：
2011-03-30
期刊：
影响因子：
--
通讯作者：
Lisberger SG
中科院分区：
文献类型：
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作者：
Chaisanguanthum KS;Lisberger SG
一个感官刺激会引起许多神经元的活动，产生一种群体反应，这种反应必须由大脑“解码”，以估计该刺激的参数。大多数解码模型都提出了复杂的神经回路，这些神经回路根据许多感觉神经元的反应来计算感觉参数的最佳估计。我们提出了一个稍微次优，但实际上更简单的解码器。解码神经元在100 ms内整合其输入;传入尖峰由起源神经元的首选刺激加权;并且局部细胞非线性近似分裂归一化，而不显式划分。次优解码器包括两个简化近似。它使用整个群体的放电率的估计，而不是计算总的群体响应，并且它使用单个神经元的局部细胞机制而不是更复杂的神经回路机制来实现分裂归一化。当应用于实际问题的估计目标速度从一个现实的模拟人口的反应在纹外视觉区MT，次优解码器具有几乎相同的准确性和精度作为传统的解码模型。它使用次优解码计算成功地预测了运动行为的精确度和不精确度，因为它只向MT中的目标速度代码添加了少量的不精确度，而MT本身是不精确的。
A sensory stimulus evokes activity in many neurons, creating a population response that must be “decoded” by the brain to estimate the parameters of that stimulus. Most decoding models have suggested complex neural circuits that compute optimal estimates of sensory parameters on the basis of responses in many sensory neurons. We propose a slightly suboptimal but practically simpler decoder. Decoding neurons integrate their inputs across 100 ms; incoming spikes are weighted by the preferred stimulus of the neuron of origin; and a local, cellular non-linearity approximates divisive normalization without dividing explicitly. The suboptimal decoder includes two simplifying approximations. It uses estimates of firing rate across the population rather than computing the total population response, and it implements divisive normalization with local cellular mechanisms of single neurons rather than more complicated neural circuit mechanisms. When applied to the practical problem of estimating target speed from a realistic simulation of the population response in extrastriate visual area MT, the suboptimal decoder has almost the same accuracy and precision as traditional decoding models. It succeeds in predicting the precision and imprecision of motor behavior using a suboptimal decoding computation because it adds only a small amount of imprecision to the code for target speed in MT, which is itself imprecise.