Pattern orthogonalization via channel decorrelation by adaptive networks.
Pattern orthogonalization via channel decorrelation by adaptive networks.
复制标题
通过自适应网络的信道去相关实现模式正交化。
DOI：
10.1007/s10827-009-0183-1
复制
发表时间：
2010
影响因子：
1.2
通讯作者：
Riecke,Hermann
中科院分区：
文献类型：
--
作者：
Wick,StuartD;Wiechert,MartinT;Friedrich,RainerW;Riecke,Hermann
神经回路对感觉信息的早期处理通常包括活动模式的重塑，这可能会促进大脑的进一步处理。例如，在嗅觉系统中，相关气味在嗅球输入端唤起的活动模式可能非常相似。然而，由于颗粒细胞和肾小球周围细胞的强烈抑制，代表嗅球输出的二尖瓣细胞的相应活动模式可能彼此显著不同。受这些结果的启发，我们研究了简单的自适应抑制网络，其目标是分离甚至正交化代表相似刺激的活动模式。由于动物在不同的时间经历不同的刺激，网络很难根据它们的相似性来学习连通性；从生物学上讲，学习是由输入通道之间的同时相关性驱动的更有可能。我们研究了模式正交化和信道去相关之间的关系，证明了如果网络同时均衡它们的输出电平，就可以通过信道去相关来实现有效的模式正交化。然而，在前馈网络中，即使是适度相似的输入模式，生物物理上看似合理的学习机制也会失效。递归网络没有这种限制；它们可以使高度相似的输入模式的表示正交化。即使当它们针对线性神经元动力学进行了优化时，当动力学是非线性时，它们也表现得非常好。这些结果提供了对简化的抑制网络的基本特征的洞察，这些基本特征可能与一般神经元电路的模式正交化相关。
The early processing of sensory information by neuronal circuits often includes a reshaping of activity patterns that may facilitate further processing in the brain. For instance, in the olfactory system the activity patterns that related odors evoke at the input of the olfactory bulb can be highly similar. Nevertheless, the corresponding activity patterns of the mitral cells, which represent the output of the olfactory bulb, can differ significantly from each other due to strong inhibition by granule cells and peri-glomerular cells. Motivated by these results we study simple adaptive inhibitory networks that aim to separate or even orthogonalize activity patterns representing similar stimuli. Since the animal experiences the different stimuli at different times it is difficult for the network to learn the connectivity based on their similarity; biologically it is more plausible that learning is driven by simultaneous correlations between the input channels. We investigate the connection between pattern orthogonalization and channel decorrelation and demonstrate that networks can achieve effective pattern orthogonalization through channel decorrelation if they simultaneously equalize their output levels. In feedforward networks biophysically plausible learning mechanisms fail, however, for even moderately similar input patterns. Recurrent networks do not have that limitation; they can orthogonalize the representations of highly similar input patterns. Even when they are optimized for linear neuronal dynamics they perform very well when the dynamics are nonlinear. These results provide insights into fundamental features of simplified inhibitory networks that may be relevant for pattern orthogonalization by neuronal circuits in general.