Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, California 94143, USA.
J Neurosci. 2011 Mar 30;31(13):4868-77. doi: 10.1523/JNEUROSCI.6776-10.2011.
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 nonlinearity 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.
感觉刺激会在许多神经元中引发活动，产生群体反应，大脑必须对其进行“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，这些回路基于许多感觉神经元的反应，计算出对感觉参数的最佳估计。我们提出了一个稍差但实际更简单的解码器。解码神经元在 100 毫秒内整合其输入，传入的尖峰由起源神经元的最佳刺激加权，局部细胞非线性近似于无显式除法的除法归一化。次优解码器包含两个简化的近似值。它使用跨群体的估计发射率，而不是计算总体群体反应，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除法归一化。当应用于从外纹状视觉区 MT 的群体反应的实际模拟中估计目标速度的实际问题时，次优解码器与传统解码模型具有几乎相同的准确性和精度。它通过使用次优解码计算成功预测了运动行为的精度和不准确性，因为它仅向 MT 中的目标速度代码添加了少量的不准确性，而 MT 本身的准确性就不高。
