Efficient computation and cue integration with noisy population codes
Efficient computation and cue integration with noisy population codes
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DOI：
10.1038/90541
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发表时间：
2001-08-01
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
大脑通过大量神经元的活动来代表感觉和运动变量。神经系统如何使用这些群体代码进行计算尚不清楚，因为单个神经元是嘈杂的，因此不可靠。我们在这里集中在两个一般类型的计算，函数逼近和线索整合，因为这些是强大的，足以处理一系列的任务，包括感觉运动变换，在感觉系统和多感官整合的特征提取。我们证明了一类特殊的神经网络，具有多维吸引子的基函数网络，可以用噪声神经元最佳地执行这两种类型的计算。此外，在我们的模型的中间层的神经元表现出类似于在几个多模态皮质区中观察到的响应特性。因此，具有多维吸引子的基函数网络可以被大脑用于利用群体代码进行有效计算。
The brain represents sensory and motor variables through the activity of large populations of neurons. It is not understood how the nervous system computes with these population codes, given that individual neurons are noisy and thus unreliable. We focus here on two general types of computation, function approximation and cue integration, as these are powerful enough to handle a range of tasks, including sensorimotor transformations, feature extraction in sensory systems and multisensory integration. We demonstrate that a particular class of neural networks, basis function networks with multidimensional attractors, can perform both types of computation optimally with noisy neurons. Moreover, neurons in the intermediate layers of our model show response properties similar to those observed in several multimodal cortical areas. Thus, basis function networks with multidimensional attractors may be used by the brain to compute efficiently with population codes.