Abstract

Despite recent advances in the reconstruction of biological neural networks, the generative principles underlying the structural properties of these networks remain incompletely understood. Neurons in the brain belonging to different functional classes, such as sensory neurons, interneurons, or motor neurons, exhibit distinct connectivity asymmetry patterns. Here, we analyze the differences in connectivity patterns among these types, focusing on the asymmetry between in-degree and out-degree. Our analysis reveals that sensory neurons tend to exhibit a predominance of outgoing connections (negative asymmetry), motor neurons a predominance of incoming connections (positive asymmetry), and interneurons a more balanced connectivity profile. To capture these type-specific features, we propose an extended network growth model in which nodes are assigned to predefined functional types, each with distinct initial attractiveness for incoming and outgoing edges. Simulations demonstrate that our model can reproduce the observed asymmetry indices of different neuron types in biological neural networks and can also generate diverse degree distribution shapes. This work offers a phenomenological generative framework that links neuron type identity to connectivity asymmetry, and it provides a baseline for future studies that incorporate additional biological constraints.

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