Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
National Vision Research Institute, The Australian College of Optometry, Melbourne, Australia.
PLoS Comput Biol. 2021 Mar 2;17(3):e1007957. doi: 10.1371/journal.pcbi.1007957. eCollection 2021 Mar.
There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.
初级视皮层（V1）中有两类截然不同的细胞：简单细胞和复杂细胞。复杂细胞的一个定义特征是其空间相位不变性；它们对具有最佳取向的定向光栅刺激反应强烈，但具有广泛的空间相位。复杂细胞完全空间相位不变性的经典模型是能量模型，其中响应是两个线性空间相位偏移滤波器的平方输出之和。然而，最近的实验研究表明，复杂细胞具有广泛的空间相位不变性，只有一部分可以用能量模型来描述。虽然已经提出了几种模型来解释复杂细胞如何学会对方向选择性但对空间相位不变，但是大多数现有的模型忽略了许多生物学上重要的细节。我们提出了一个基于自然场景刺激呈现的基于简单细胞输入的复杂细胞学习模型。该模型是一个具有基于率的神经元的三层网络，描述了外侧膝状体细胞（第 1 层）、V1 简单细胞（第 2 层）和 V1 复杂细胞（第 3 层）的活动。前两层实现了一个最近提出的具有生物学合理性并解释了许多实验现象的简单细胞模型。复杂细胞的神经动力学被建模为简单细胞输入的整合以及响应归一化。外侧膝状体和简单细胞之间的连接是使用赫布和反赫布可塑性学习的。简单细胞和复杂细胞之间的连接是使用改进的 Bienenstock、Cooper 和 Munro（BCM）规则学习的。我们的结果表明，学习规则可以描述多种类似于实验中观察到的复杂细胞。
