Publications

Publications resulting from this project

2023

1. Rueckauer, B. and van Gerven, M. "An In-Silico Framework for Modeling Optimal Control of Neural Systems", Frontiers in Neuroscience 17: 332, 2023, doi: 10.3389/fnins.2023.1141884.

2. Odenthal, M., Claar, V., Paul, O., Ruther, P. "Hierarchical Bonding Yield Test Structure for Flexible High Channel-Count Neural Probes Interfacing ASIC Chips", 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Jan. 15-19 2023, Munich, Germany, pp. 409-412. (MEMS 23 Best Student Paper Finalist - Poster)

3. Sharma, K., Boehler, C., Saeed, A., Paul, O., Asplund, M., Ruther, P. "Wafer-level Fabrication Process of Ultrathin Silicon Insertion Shuttles for Flexible Neural Implants", 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Jan. 15-19 2023, Munich, Germany, pp. 421-424.

2022

1. Chen, X.*, Morales-Gregorio, A.*, Sridhar, S., Sprenger, J., van Albada, S. J., Grün, S., Roelfsema, P. "1024-channel electrophysiological recordings in macaque V1 and V4 during resting state", Sci Data 9, 77, 2022, doi.org/10.1038/s41597-022-01180-1. 

2. Küçükoğlu, B., Rueckauer, B., Ahmad, N., van Gerven, M. "Optimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning", International Journal of Neural Systems, 2022, doi.org/10.1142/S0129065722500526.

3. Otte, E., Vlachos, A., Asplund, M. “Engineering strategies towards overcoming bleeding and glial scar formation around neural probes”, Cell Tissue Res 387, 461–477, 2022, doi.org/10.1007/s00441-021-03567-9. 

4. Hadorn, J.*, Wang, Z.*, Rueckauer, B., Chen, X., Roelfsema, P., Liu, S.-C. "Fast temporal decoding from large-scale neural recordings in monkey visual cortex", NeurIPS 2022 Workshop on Shared Visual Representations in Human & Machine Intelligence (SVRHM Workshop), Dec 2, 2022.

5. Rueckauer, B. and van Gerven, M. "Experiencing Prosthetic Vision with Event-Based Sensors", Proceedings of the International Conference on Neuromorphic Systems, 2022, doi.org/10.1145/3546790.3546813.

6. van der Grinten, M. L., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rueckauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., Güçlütürk, Y. "Biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses", bioRxiv, 2022, under review.

2021

1. Fernandez, E., Alfaro, A., Soto-Sanchez, C., Gonzalez-Lopez, P., Lozano, A., Peña, S., Rodil, A., Gomez, B., Chez, X., Roefsema, P., Rolston, J., Davis, T., Normann, RA. "Visual percepts evoked with an intracortical 96-channel microelectrode array inserted in human occipital cortex", The Journal of clinical investigation 131.23, 2021, doi.org/10.1172/JCI151331.

2. Wang, H., Mohamed, H., Wang, Z., Rueckauer, B., Liu, S.-C. “LiteEdge: Lightweight Semantic Edge Detection Network”, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021.

Related publications

Aimar, A. et al. (2019) ‘NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps’, IEEE Transactions on Neural Networks and Learning Systems, 30(3), pp. 644–656.

Bahramisharif, A. and Van Gerven, M. (2010) ‘Covert attention allows for continuous control of brain–computer interfaces’, European Journal of. Available at: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1460-9568.2010.07174.x.

Barz, F. et al. (2020) ‘CMOS-Compatible, Flexible, Intracortical Neural Probes’, IEEE Transactions on Bio-medical Engineering, 67(5), pp. 1366–1376.

Boehler, C. et al. (2020) ‘Tutorial: guidelines for standardized performance tests for electrodes intended for neural interfaces and bioelectronics’, Nature protocols. doi: 10.1038/s41596-020-0389-2.

Chen, X. et al. (2020) ‘Shape perception via a high-channel-count neuroprosthesis in monkey visual cortex’, Science, 370(6521), pp. 1191–1196.

Gao, C. et al. (2018) ‘DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator’, in Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. New York, NY, USA: Association for Computing Machinery (FPGA ’18), pp. 21–30.

Gao, C. et al. (2020) ‘Recurrent Neural Network Control of a Hybrid Dynamical Transfemoral Prosthesis with EdgeDRNN Accelerator’, 2020 IEEE International Conference on Robotics and Automation (ICRA). doi: 10.1109/icra40945.2020.9196984.

Güçlü, U. and van Gerven, M. A. J. (2015) ‘Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations Across the Ventral Stream’, Journal of Neuroscience, 35(27), pp. 10005–10014.

Güçlü, U. and van Gerven, M. A. J. (2016) ‘Modeling the dynamics of human brain activity with recurrent neural networks’, Frontiers in systems neuroscience, pp. 1–19.

Schoenmakers, S. et al. (2013) ‘Linear reconstruction of perceived images from human brain activity’, NeuroImage. Available at: https://www.sciencedirect.com/science/article/pii/S1053811913007994.