Willmore Ben, Smyth Darragh
Department of Physiology, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.
Network. 2003 Aug;14(3):553-77.
Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods-spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution ('reginv') is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment-the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.
最近的研究从视觉皮层中简单细胞对自然场景刺激的反应中恢复了感受野图谱。与传统的人工刺激相比，自然场景具有许多理论和实际优势；然而，感受野估计方法比白噪声刺激更为复杂。在这里，我们描述并论证了其中几种方法——反向相关估计的频谱校正、直接最小二乘解、迭代最小二乘算法和正则化最小二乘解。我们研究了不同方法的优缺点，并在对模拟简单细胞数据的直接比较中对它们进行了评估。这表明，至少对于准线性模拟简单细胞来说，正则化解（“reginv”）效率最高，在对一阶核进行高分辨率重建时需要的刺激呈现次数更少。我们还研究了决定这类实验成功与否的几个实际问题——神经元非线性、反应变异性和刺激方案选择的影响。
