标题： 具有仿射特征响应变换的神经编码 显示英文标题

标题： Neural encoding with affine feature response transforms

摘要： 当前的线性化编码模型在预测神经对感官输入的响应时，通常忽略了可能提高模型效率和可解释性的神经科学启发式约束。 为了解决这个问题，我们提出了一种新方法，称为仿射特征响应变换（AFRT），该方法利用了大脑的视网膜拓扑组织。 将AFRT应用于猕猴大脑V1、V4和IT区域的多单位活动，我们证明AFRT通过将每个神经元的感受野分割为仿射视网膜变换，然后是局部特征响应，减少了冗余计算并提高了当前线性化编码模型的性能。 值得注意的是，通过将感受野分解为具有三个可解释参数（用于平移和缩放）的顺序仿射成分以及每个响应中具有少量特征权重的响应成分，AFRT相比非结构化模型实现了数量级更少的参数进行编码。 我们表明每个神经元编码的视网膜变换与大脑的感受野高度一致。 综上所述，这些发现表明，这个空间变换器网络中的新子集可以在自然刺激的神经编码模型中发挥重要作用。 显示英文摘要

摘要： Current linearizing encoding models that predict neural responses to sensory input typically neglect neuroscience-inspired constraints that could enhance model efficiency and interpretability. To address this, we propose a new method called affine feature response transform (AFRT), which exploits the brain's retinotopic organization. Applying AFRT to encode multi-unit activity in areas V1, V4, and IT of the macaque brain, we demonstrate that AFRT reduces redundant computations and enhances the performance of current linearizing encoding models by segmenting each neuron's receptive field into an affine retinal transform, followed by a localized feature response. Remarkably, by factorizing receptive fields into a sequential affine component with three interpretable parameters (for shifting and scaling) and response components with a small number of feature weights per response, AFRT achieves encoding with orders of magnitude fewer parameters compared to unstructured models. We show that the retinal transform of each neuron's encoding agrees well with the brain's receptive field. Together, these findings suggest that this new subset within spatial transformer network can be instrumental in neural encoding models of naturalistic stimuli.