Publications resulting from this project

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: