Part-time Assistant Professor in Brain-Inspired AI at the Donders Institute (Radboud University).

Working on Efficient AI/ML, Brain-Inspired Computing, Computational Neuroscience and more.

Part-time curious Human Being at Earth (various locations).

Refurbishes/repairs (race) bikes in spare time, with a personal affinity for traditional ’70s steel frames. Fixing and creating things (electronic/mechanical/otherwise) is particularly enjoyed. Attempting to figure out self: an entangled mixture of Western and Eastern (British-Pakistani), working and middle-class, intellectual and spiritual, and more.

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Scientific Publications

2023

Dalm, S., van Gerven, M., & Ahmad, N. (2023). Effective Learning with Node Perturbation in Deep Neural Networks. In arXiv [cs.LG]. arXiv. http://arxiv.org/abs/2310.00965

Ahmad, N., Schrader, E.., van Gerven, M. (2023), Constrained Parameter Inference as a Principle for Learning. Transactions on Machine Learning Research, 2835-8856.

Dalm, S., Ahmad, N., Ambrogioni, L., van Gerven, M. (2023), Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks. Transactions on Machine Learning Research, 2835-8856.

2022

Küçükoğlu, B., Borkent, W., Rueckauer, B., Ahmad, N., Güçlü, U., & van Gerven, M. (2022). Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization. In arXiv [cs.LG]. arXiv.

Küçükoğlu, B., Rueckauer, B., Ahmad, N., van Steveninck, J. de R., Güçlü, U., & van Gerven, M. (2022). Optimization of Neuroprosthetic Vision via End-to-End Deep Reinforcement Learning. International Journal of Neural Systems, 32(11), 2250052.

Ali, A., Ahmad, N., de Groot, E., Johannes van Gerven, M. A., & Kietzmann, T. C. (2022). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns , 3(12), 100639.

2021

Ahmad, N., Rueckauer, B., & van Gerven, M. (2021). Brain-inspired learning drives advances in neuromorphic computing. ERCIM News, 125, 24–25.

Dalm, S., Ahmad, N., Ambrogioni, L., & van Gerven, M. (2021). Scaling up learning with GAIT-prop. arXiv Preprint arXiv:2102. 11598.

2020

Ahmad, N., van Gerven, M. A., & Ambrogioni, L. (2020). Gait-prop: A biologically plausible learning rule derived from backpropagation of error. Advances in Neural Information Processing Systems, 33, 10913–10923.

Ahmad, N., Ambrogioni, L., & van Gerven, M. A. J. (2020). Overcoming the Weight Transport Problem via Spike-Timing-Dependent Weight Inference. arXiv Preprint arXiv:2003. 03988.

2018

Ahmad, N., Isbister, J. B., Smithe, T. S. C., & Stringer, S. M. (2018). Spike: A gpu optimised spiking neural network simulator. bioRxiv, 461160.

Isbister, J. B., Eguchi, A., Ahmad, N., Galeazzi, J. M., Buckley, M. J., & Stringer, S. (2018). A new approach to solving the feature-binding problem in primate vision. Interface Focus, 8(4), 20180021.

Eguchi, A., Isbister, J. B., Ahmad, N., & Stringer, S. (2018). The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system. Psychological Review, 125(4), 545.

2016

Ahmad, N., Higgins, I., Walker, K. M. M., & Stringer, S. M. (2016). Harmonic training and the formation of pitch representation in a neural network model of the auditory brain. Frontiers in Computational Neuroscience, 10, 24.

2013

Ahmad, N., Szymkowiak, A., & Campbell, P. (2013). Keystroke dynamics in the pre-touchscreen era. Frontiers in Human Neuroscience, 7, 835.