Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, BNRist, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China, and Key Laboratory of Image Processing and Intelligent Control, Education Ministry of China, Wuhan 430074, China
Neural Comput. 2021 Dec 15;34(1):104-137. doi: 10.1162/neco_a_01453.
The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.
初级视皮层 (V1) 神经元的功能特性被认为与其网络的结构特性密切相关，但具体关系尚不清楚。先前的理论研究表明，稀疏编码作为一种节能的编码方法，可能是 V1 神经元方向选择性的基础。因此，我们旨在阐明神经元是如何连接以产生这种特征的。我们构建了一个模型，并赋予它一个简单的赫布学习规则，以对自然场景的图像进行编码。兴奋神经元对图像的反应稀疏，并表现出强烈的方向选择性。在学习之后，兴奋神经元对、抑制神经元对和兴奋-抑制神经元对之间的连接取决于神经元的放电模式和感受野相似性。兴奋神经元和抑制神经元的感受野分别由其前兴奋神经元和抑制神经元的感受野很好地预测。兴奋神经元形成了一个小世界网络，其中某些局部连接模式显著过表达。双向操纵抑制神经元的放电率会导致兴奋神经元的放电率发生线性变换，反之亦然。这些连接特性和调制效应与 V1 中测量的各种数据一致，表明稀疏编码原理可能是 V1 神经元的功能和连接特性的基础。
