Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns
Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns
复制标题
预测猴子纹状皮层非线性神经元对复杂模式的反应
作者：
S. Lehky;T. Sejnowski;R. Desimone
灵长类视觉皮层中的绝大多数神经元都是非线性的。对于这些细胞，线性系统分析的技术，与一些成功的模型视网膜神经节细胞和纹状体简单的细胞，是有限的适用性。作为了解非线性视觉神经元特性的开始，我们记录了纹状体复合细胞对数百张图像的反应，包括简单刺激（条形和正弦曲线）以及复杂刺激（随机纹理和3-D阴影表面）。后一组往往给出最强烈的反应。我们使用迭代优化算法为每个神经元创建了一个神经网络模型。记录的对一些刺激模式（训练集）的反应被用来创建模型，而对其他模式的反应被保留用于测试网络。该网络预测了对训练集模式的记录响应，相关性中位数为0.95。他们能够预测对不在训练集中的测试刺激的反应，总体相关性为0.78，单独考虑复杂刺激的相关性为0.65。因此，他们能够捕获神经元的大部分输入/输出传递函数，即使是复杂的模式。研究每个网络内部的连接强度，网络的不同部分似乎在不同的空间尺度上处理信息。为了获得进一步的见解，网络模型被反转以构建每个细胞的“最佳”刺激，并且它们的感受野被映射为高分辨率点。复杂细胞的感受野特性不能被简化为比网络模型本身更简单的数学公式。
The overwhelming majority of neurons in primate visual cortex are nonlinear. For those cells, the techniques of linear system analysis, used with some success to model retinal ganglion cells and striate simple cells, are of limited applicability. As a start toward understanding the properties of nonlinear visual neurons, we have recorded responses of striate complex cells to hundreds of images, including both simple stimuli (bars and sinusoids) as well as complex stimuli (random textures and 3-D shaded surfaces). The latter set tended to give the strongest response. We created a neural network model for each neuron using an iterative optimization algorithm. The recorded responses to some stimulus patterns (the training set) were used to create the model, while responses to other patterns were reserved for testing the networks. The networks predicted recorded responses to training set patterns with a median correlation of 0.95. They were able to predict responses to test stimuli not in the training set with a correlation of 0.78 overall, and a correlation of 0.65 for complex stimuli considered alone. Thus, they were able to capture much of the input/output transfer function of the neurons, even for complex patterns. Examining connection strengths within each network, different parts of the network appeared to handle information at different spatial scales. To gain further insights, the network models were inverted to construct “optimal” stimuli for each cell, and their receptive fields were mapped with high-resolution spots. The receptive field properties of complex cells could not be reduced to any simpler mathematical formulation than the network models themselves.
影响因子：
5.5
作者：
DERRINGTON, AM;LENNIE, P
通讯作者：
LENNIE, P