Published:

• Gwilliams, L., Bhaya-Grossman, I., Zhang, Y., Scott, T., Harper, S., & Levy, D. (2024). Computational Architecture of Speech Comprehension in the Human Brain. Annual Review of Linguistics, 11. 

• Choi, M., Zhang, Y., Han, K., & Liu, Z. (2024). Human Eyes–Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises. Neural Computation, 36(9), 1713-1743.

• Choi, M., Han, K., Wang, X., Zhang, Y., & Liu, Z. (2023). A dual-stream neural network explains the functional segregation of dorsal and ventral visual pathways in human brains. Advances in Neural Information Processing Systems, 36, 50408-50428.

• Zhang, Y., Choi, M., Han, K., & Liu, Z (2021). Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning. Advances in Neural Information Processing Systems, 34, 18513-18526.

• Kim, J. H., Zhang, Y., Han, K., Wen, Z., Choi, M., & Liu, Z. (2021). Representation learning of resting state fMRI with variational autoencoder. NeuroImage, 241, 118423.

• Zhang, Y., Kim, J. H., Brang, D., & Liu, Z. (2021). Naturalistic stimuli: A paradigm for multiscale functional characterization of the human brain. Current Opinion in Biomedical Engineering, 100298.

• Zhang, Y., Han, K., Worth, R., Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nat Commun 11, 1877. https://doi.org/10.1038/s41467-020-15804-w.

• Han, K., Wen, H., Zhang, Y., Fu D., Culurciello E., Liu Z. (2018). Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition. NIPS.

• Wen, H., Han, K., Shi, J., Zhang, Y., Culurciello, E., & Liu, Z. (2018). Deep predictive coding network for object recognition. ICML, 80:5263-5272, 2018

• Han, K., Wen, H., Shi, J., Lu, K. H., Zhang, Y., & Liu, Z. (2018). Variational autoencoder: An unsupervised model for modeling and decoding fMRI activity in visual cortex. NeuroImage, 198: 125-136.

• Shi, J., Wen, H., Zhang, Y., Han, K., & Liu, Z. (2018). Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision. Human brain mapping, 39(5), 2269-2282.

• Lynch, L. K., Lu, K. H., Wen, H., Zhang, Y., Saykin, A. J., & Liu, Z. (2018). Task‐evoked functional connectivity does not explain functional connectivity differences between rest and task conditions. Human brain mapping, 39(12), 4939-4948.

• Zhang, Y., Chen, G., Wen, H., Lu, K. H., & Liu, Z. (2017). Musical imagery involves wernicke's area in bilateral and anti-correlated network interactions in musicians. Scientific reports, 7(1), 17066.

• Wen, H., Shi, J., Zhang, Y., Lu, K. H., Cao, J., & Liu, Z. (2017). Neural encoding and decoding with deep learning for dynamic natural vision. Cerebral Cortex, 28(2): 4136-4160, 2018.

In preparation:

• Zhang, Y., Leonard, M. K., Gwilliams, L., Bhaya-Grossman, I., Chang E.F. Word Extraction in Human Speech Cortex. (In preparation).

• Zhang, Y., Han, K., Choi, M., & Liu, Z. Visually grounded word representations reveal principal semantic axes explaining conceptual organization in the human brain. (In preparation).

• OHBM 2018 (Oral): Zhang, Y., Kim, J.H., Wen, H. & Liu, Z. High Gamma Electrocorticography in Superior Temporal Gyrus Represents Words During Natural Speech. Video / Purdue BME News

• Deep Learning meets Neuroscience Lecture Series at Trinity College Dublin, organized by Cusack Lab (Invited talk, Nov 2020): Deep Learning for Encoding and Decoding Human Brain Activity during Natural Vision and Language Comprehension. Video