Department of Electrical Engineering, Stanford University, Stanford, United States.
Department of Physics, Stanford University, Stanford, United States.
Elife. 2020 Mar 9;9:e45743. doi: 10.7554/eLife.45743.
Responses of sensory neurons are often modeled using a weighted combination of rectified linear subunits. Since these subunits often cannot be measured directly, a flexible method is needed to infer their properties from the responses of downstream neurons. We present a method for maximum likelihood estimation of subunits by soft-clustering spike-triggered stimuli, and demonstrate its effectiveness in visual neurons. For parasol retinal ganglion cells in macaque retina, estimated subunits partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits between neighboring cells, producing a parsimonious population model. Closed-loop validation, using stimuli lying in the null space of the linear receptive field, revealed stronger nonlinearities in OFF cells than ON cells. Responses to natural images, jittered to emulate fixational eye movements, were accurately predicted by the subunit model. Finally, the generality of the approach was demonstrated in macaque V1 neurons.
感觉神经元的反应通常使用整流线性亚基的加权组合来建模。由于这些亚基通常无法直接测量，因此需要一种灵活的方法从下游神经元的反应中推断它们的特性。我们提出了一种通过软聚类尖峰触发刺激来进行亚基最大似然估计的方法，并在视觉神经元中证明了其有效性。对于猕猴视网膜中的伞状视网膜神经节细胞，估计的亚基将感受野划分为紧凑的区域，可能代表聚集的双极细胞输入。联合聚类揭示了相邻细胞之间共享的亚基，产生了一个简约的群体模型。使用线性感受野的零空间中的刺激进行闭环验证，揭示了 OFF 细胞比 ON 细胞具有更强的非线性。通过亚基模型可以准确预测自然图像的抖动，以模拟固视眼球运动的响应。最后，该方法的通用性在猕猴 V1 神经元中得到了证明。
