We are excited to feature the recent publications of our members, highlighting their valuable research contributions to the field of neuroscience:
Luo, X., Rechardt, A., Sun, G., Nejad, K. K., Yáñez, F., Yilmaz, B., … & Love, B. C. (2024). Large language models surpass human experts in predicting neuroscience results. arXiv preprint arXiv:2403.03230.
Farhad S, Metin SZ, Uyulan Ç, Makouei STZ, Metin B, Ergüzel TT, Tarhan N. Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data. Clin EEG Neurosci. 2024 Jan 9:15500594231222980. doi: 10.1177/15500594231222980. Epub ahead of print. PMID: 38192213.
Alishbayli, A., Schlegel, N. J., & Englitz, B. (2023). Using auditory texture statistics for domain-neutral removal of background sounds. Frontiers in Audiology and Otology, 1, 1226946.
Lao-Rodríguez, A. B., Przewrocki, K., Pérez-González, D., Alishbayli, A., Yilmaz, E., Malmierca, M. S., & Englitz, B. (2023). Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. Science Advances, 9(24). https://doi.org/10.1126/sciadv.abq8657
Metin, B., Farhad, S., Erguzel, T. T., Çiftçi, E., & Tarhan, N. (2023). Combined use of gray matter volume and neuropsychological test performance for classification of individuals with bipolar I disorder via artificial neural network method. Journal of Neural Transmission. https://doi.org/10.1007/s00702-023-02649-y
Maclean, M., Muradov, J., Greene, R., & Friedman, A. (2023). 128 NMDA-Receptor Antagonism for the Prevention of Neurological Dysfunction in Traumatic Brain Injury: Results of a Randomized Pre-Clinical Trial. Neurosurgery, 69(Supplement_1), 32. https://doi.org/10.1227/neu.0000000000002375_128
Metin B, Uyulan Ç, Ergüzel TT, et al. The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data. Clinical EEG and Neuroscience. 2022;0(0).
Uyulan, C., Erguzel, T. T., Turk, O., Farhad, S., Metin, B., & Tarhan, N. (2022). A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. Clinical Eeg and Neuroscience, 54(2), 151–159. https://doi.org/10.1177/15500594221122699
