Gundavarapu Anila, Chakravarthy V Srinivasa
Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, India.
Center for Complex Systems and Dynamics, Indian Institute of Technology Madras, Chennai, India.
Front Neurosci. 2023 May 22;17:1154252. doi: 10.3389/fnins.2023.1154252. eCollection 2023.
Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral ("what") pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal ("where") pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway.
尽管有大量的建模文献致力于灵长类视觉系统腹侧（“什么”）通路的物体识别过程，但对背侧（“哪里”）通路中诸如内侧颞上区（MST）等运动敏感区域的建模研究相对较少。猕猴MST区域的神经元对不同类型的光流序列（如径向流和旋转流）有选择性反应。我们提出了三个模型，旨在模拟MST神经元执行的光流计算。模型1和模型2各由三个阶段组成：方向选择性镶嵌网络（DSMN）、细胞平面网络（CPNW）或赫布网络（HBNW）以及光流网络（OF）。这三个阶段大致分别对应于灵长类运动通路中的V1 - MT - MST区域。这两个模型都使用赫布规则的生物学合理变体逐阶段进行训练。模拟结果表明，模型1和模型2中的神经元（在平移、径向和旋转序列上进行训练）产生的反应可以解释神经生物学上发现的MSTd细胞特性。另一方面，模型3由速度选择性镶嵌网络（VSMN）后跟一个卷积神经网络（CNN）组成，该卷积神经网络使用监督反向传播算法在径向和旋转序列上进行训练。由卷积层和最后隐藏层反应构成的反应相似性矩阵（RSM）的定量比较表明，模型3的神经元反应与猕猴运动通路中的功能层次概念一致。这些结果还表明，深度学习模型可以提供一种计算优雅且生物学上合理的解决方案，以模拟灵长类运动通路皮质反应的发展。
