Wiltschut Jan, Hamker Fred H
Psychology and Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, Westf. Wilhelms-Universität Münster, Münster, Germany.
Vis Neurosci. 2009 Jan-Feb;26(1):21-34. doi: 10.1017/S0952523808080966. Epub 2009 Feb 10.
Efficient coding has been proposed to play an essential role in early visual processing. While several approaches used an objective function to optimize a particular aspect of efficient coding, such as the minimization of mutual information or the maximization of sparseness, we here explore how different estimates of efficient coding in a model with nonlinear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, that is, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data. However, the degree of the correlation varies with the different estimates of efficient coding, and in particular, the variance in the firing pattern of each cell does not predict a similarity of model and experimental data.
高效编码被认为在早期视觉处理中起着至关重要的作用。虽然有几种方法使用目标函数来优化高效编码的特定方面，例如最小化互信息或最大化稀疏性，但我们在此探讨在具有非线性动力学和赫布学习的模型中，不同的高效编码估计如何决定模型感受野与V1数据在空间调谐方面的相似性。我们的模拟结果表明，大多数高效编码度量与模型感受野数据和V1数据的相似性相关，也就是说，优化高效编码估计会增加模型数据与实验数据的相似性。然而，相关程度随高效编码的不同估计而变化，特别是每个细胞放电模式的方差并不能预测模型和实验数据的相似性。
