Publications resulting from this project


  1. Chen X*, Morales-Gregorio A*, Sridhar S, Sprenger J, van Albada SJ, Grün S, Roelfsema PR. 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state (in press, Scientific Data).

  2. Burcu Küçükoğlu, Bodo Rueckauer, Nasir Ahmad, Marcel van Gerven. Optimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning (submitted to Journal of Neural Engineering, preprint available:

  3. Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer, Nasir Ahmad, Umut Güçlü, Marcel van Gerven. P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization (pre-print).

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


  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. J. Clin, Investigation 2021.

  2. Hao Wang, Hasan Mohamed, Zuowen Wang, Bodo Rueckauer, Shih-Chii Liu, “LiteEdge: Lightweight Semantic Edge Detection Network”, 2021 ICCV Workshop on 1st Workshop on Video Scene Parsing in the Wild, Oct 11-17, 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:

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: