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. Head of the Laboratory for Efficient Neural Systems (LENS).

Part-time Curious Human Being on Earth.

Personal affinity for fixing traditional ’70s steel frames and building things from wood and metal. Facilitator in training with Awaken to Meaning.

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Scientific Publications

2026

Huebotter, J., Thill, S., van Gerven, M., & Ahmad, N. (2026). An Educated Guess: Deriving Statistically Aligned Gradient Estimates for Zeroth-Order Optimisation. Advances in Neural Information Processing Systems, 39 (accepted).

Dalm, S., van Gerven, M., & Ahmad, N. (2026). Node perturbation can effectively train multi-layer neural networks. Neuromorphic Computing and Engineering, 6(3), 034020. https://doi.org/10.1088/2634-4386/ae9be4

García Fernández, J., Ahmad, N., & van Gerven, M. (2026). Noise-based reward-modulated learning. Neuromorphic Computing and Engineering, 6(3), 034001.

Agliati, P., Urbano, A., Lanillos, P., Ahmad, N., van Gerven, M., & Keemink, S. (2026). Spiking neurons as predictive controllers of linear systems. PLOS Computational Biology, 22(7), e1014432.

Hahury, M., & Ahmad, N. (2026). Two for the price of one: Comparing one-sided and two-sided perturbations in n-SPSA for transformer fine-tuning and training from scratch. In The 38th Benelux Conference on Artificial Intelligence and the 35th Belgian Dutch Conference on Machine Learning (BNAIC/BeNeLearn 2026).

Gözükara, D., Ahmad, N., Oetringer, D., & Geerligs, L. (2026). Memory and hippocampal responses to event boundaries are modulated by global brain states. bioRxiv.

Gözükara, D., Ahmad, N., Oetringer, D., & Geerligs, L. (2026). The metastable mind: Neural underpinnings of naturalistic cognition through the synthesis of event segmentation and metastable neural states. In arXiv. http://arxiv.org/abs/2605.31473

2025

Koenders, K., Schnitzpan, L., Kammerbauer, F., Shu, S., Jakob, G., Kläui, M., Mentink, J. H., Ahmad, N., & van Gerven, M. (2025). Noise-based local learning using stochastic magnetic tunnel junctions. Physical Review Applied, 23(5), 054035. https://doi.org/10.1103/PhysRevApplied.23.054035

García Fernández, J., Ahmad, N., & van Gerven, M. (2025). A unified perspective on optimization in machine learning and neuroscience: From gradient descent to neural adaptation. In arXiv. http://arxiv.org/abs/2510.18812

Gözükara, D., Oetringer, D., Ahmad, N., & Geerligs, L. (2025). Multi-scale anti-correlated neural states dominate naturalistic whole-brain activity. bioRxiv.

Gözükara, D., Ahmad, N., Seeliger, K., Oetringer, D., & Geerligs, L. (2025). Neural network-based encoding in free-viewing fMRI with gaze-aware models. bioRxiv.

2024

García Fernández, J., Ahmad, N., & van Gerven, M. (2024). Ornstein-Uhlenbeck adaptation as a mechanism for learning in brains and machines. Entropy, 26(12), 1125.

Ahmad, N. (2024). Correlations are ruining your gradient descent. In arXiv [cs.LG]. http://arxiv.org/abs/2407.10780

Offergeld, J., van Gerven, M., & Ahmad, N. (2024). Subspace node pruning. In arXiv [cs.LG]. http://arxiv.org/abs/2405.17506

Dalm, S., Offergeld, J., Ahmad, N., & van Gerven, M. (2024). Efficient deep learning with decorrelated backpropagation. In arXiv [cs.LG]. http://arxiv.org/abs/2405.02385

2023

Ahmad, N., Schrader, E., & van Gerven, M. (2023). Constrained parameter inference as a principle for learning. Transactions on Machine Learning Research.

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.

2022

Ali, A., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2022). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns, 3(12), 100639. https://doi.org/10.1016/j.patter.2022.100639

Küçükoğlu, B., Rueckauer, B., Ahmad, N., de Ruyter van Steveninck, J., 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.

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]. http://arxiv.org/abs/2211.06236

2021

Ahmad, N., Ambrogioni, L., & van Gerven, M. A. J. (2021). Overcoming the weight transport problem via spike-timing-dependent weight inference. Neurons, Behavior, Data Analysis, and Theory, 5(3), 1–20. https://doi.org/10.51628/001c.27423

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. In arXiv [cs.LG]. http://arxiv.org/abs/2102.11598

2020

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

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. A. (2013). Keystroke dynamics in the pre-touchscreen era. Frontiers in Human Neuroscience, 7, 835.