Research
Journal papers
X.-Y. Zhang, S. Bobadilla-Suarez, X. Luo, M. Lemonari, S. L. Brincat, M. Siegel, E. K. Miller and B. C. Love. Nature Communications. (in press)
Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model's representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.
Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model's representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.
X.-Y. Zhang*, Y. Yao, Z. Han and G. Yan. Communications Physics, 2025. (* corresponding author)
Most real-world complex networks are spatially embedded, with nodes positioned in physical space. In such systems, distance-based connectivity shapes not only the information transmission efficiency but also the network robustness and vulnerability behavior. Here, we systematically examined how spatial distance influences network robustness using k-core percolation across a pair of models of long-range connectivity. For structural connected components, we found that long-range connectivity can trigger explosive phase transitions, but with a delayed threshold. For spatial neighborhoods, two core phenomena emerge: spatial diffusion and clustering. Despite varying connectivity models, spatial neighborhoods consistently demonstrate self-organized criticality throughout the percolation process.
Most real-world complex networks are spatially embedded, with nodes positioned in physical space. In such systems, distance-based connectivity shapes not only the information transmission efficiency but also the network robustness and vulnerability behavior. Here, we systematically examined how spatial distance influences network robustness using k-core percolation across a pair of models of long-range connectivity. For structural connected components, we found that long-range connectivity can trigger explosive phase transitions, but with a delayed threshold. For spatial neighborhoods, two core phenomena emerge: spatial diffusion and clustering. Despite varying connectivity models, spatial neighborhoods consistently demonstrate self-organized criticality throughout the percolation process.
X.-Y. Zhang, X. Ru, Z. Liu, J. M. Moore and G. Yan. Journal of Physics: Complexity. (accepted)
X.-Y. Zhang*, G. Yan and J. M. Moore*. Journal of Complex Networks, 2025. (* corresponding author)
X.-Y. Zhang, J. M. Moore, X. Ru and G. Yan. Physical Review Letters, 2024. Editors' Suggestion & Featured in Physics & Physical Review Letters collection of the year 2024
We uncovered a fundamental and elegant scaling law in the synapse-resolution Drosophila connectomes. This discovery challenges the well-known exponential distance rule previously established in inter-areal brain networks and carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Our findings establish a direct link between brain geometry and topology, hinting at new opportunities for developing brain geometry-inspired artificial intelligence.
We uncovered a fundamental and elegant scaling law in the synapse-resolution Drosophila connectomes. This discovery challenges the well-known exponential distance rule previously established in inter-areal brain networks and carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Our findings establish a direct link between brain geometry and topology, hinting at new opportunities for developing brain geometry-inspired artificial intelligence.
X.-Y. Zhang, J. Sun and G. Yan. Physical Review Research, 2021.
We explored a general model of temporal networks and analytically proved that the weight variation of a link is equivalent to attaching a virtual driver node to that link. Consequently, the temporality of link weights can significantly increase the dimension of controllable space and remarkably reduce control cost.
We explored a general model of temporal networks and analytically proved that the weight variation of a link is equivalent to attaching a virtual driver node to that link. Consequently, the temporality of link weights can significantly increase the dimension of controllable space and remarkably reduce control cost.
Z. Liu, X. Ru, J. M. Moore, X.-Y. Zhang and G. Yan. IEEE Transactions on Network Science and Engineering, 2024.
Conference papers
X. Ru, X.-Y. Zhang, Z. Liu, J. M. Moore and G. Yan. NeurIPS, 2023. Spotlight
X. Ru, J. M. Moore, X.-Y. Zhang, Y. Zeng and G. Yan. AAAI, 2023. Oral
Preprints
X.-Y. Zhang, H. Lin, Z. Deng, M. Siegel, E. K. Miller, G. Yan. bioRxiv.
When we encounter familiar environments, the brain may trigger neuronal activities that resemble specific patterns. Our findings show that the machine effectively captures the function of individual brain regions as a consequence of minimizing visual errors. Specifically, in areas related to vision, V4 and LIP are implicated in visual color and shape processing, while MT is more involved in visual motion. Moreover, we highlight the importance of the source brain region in both decoding and encoding processes. While each brain region harbors some global information, achieving good performance in vision reconstruction necessitates utilizing data from regions closely linked with vision processing.
When we encounter familiar environments, the brain may trigger neuronal activities that resemble specific patterns. Our findings show that the machine effectively captures the function of individual brain regions as a consequence of minimizing visual errors. Specifically, in areas related to vision, V4 and LIP are implicated in visual color and shape processing, while MT is more involved in visual motion. Moreover, we highlight the importance of the source brain region in both decoding and encoding processes. While each brain region harbors some global information, achieving good performance in vision reconstruction necessitates utilizing data from regions closely linked with vision processing.
Presentations
- Invited talk at the 8th National Conference on Statistical Physics and Complex Systems 2025, Ningbo, China.
Heavy-tailed update arises from information-driven self-organization in non-equilibrium learning. - Poster presentation at the 17th Annual Meeting of Chinese Neuroscience Society 2024, Suzhou, China.
Adaptive stretching of representations across brain regions and deep learning model layers. - Oral presentation at 20th China Networks Science Forum 2024, Beijing, China.
Geometric scaling law in real neuronal networks. - Spotlight at International Conference NeurIPS 2023, New Orlean, USA.
Attentive transfer entropy to exploit transient emergence of coupling effect. - Oral presentation at International Conference NetSci 2022, Shanghai, China.
Link weight variation offers superiority in controlling temporal networks. - Oral presentation at International Conference NetSci-X 2018, Hangzhou, China.
Structural origin of co-susceptibility in cascading failures. We found that both structural closeness and high-order correlations could lead to co-susceptibility. This finding prompted us to propose a new statistical quantity, based on structure only, to assess the co-susceptibility of node pairs in an arbitrary network.