Ammar W. Mohemmed

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About

Ammar W. Mohemmed is a researcher whose work lies at the intersection of computational neuroscience and genetic modelling, with a particular focus on understanding how genes influence neural dynamics. His key research areas include spiking neural networks, neurogenetic modelling, and the probabilistic behaviour of neural systems. Mohemmed’s major contribution is the development of computational frameworks that integrate genetic factors into spiking neural network models, allowing for a more biologically realistic simulation of how genes modulate neural activity and learning. His most-cited paper, "Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling" (2012), has garnered 6 citations and serves as a foundational piece in the emerging field of computational neurogenetics. This work demonstrates his ability to bridge abstract genetic concepts with dynamic neural computation, offering new tools for exploring neurological disorders and brain-inspired AI. Mohemmed’s research is notable for its interdisciplinary approach, combining genetics, neuroscience, and machine learning, making it valuable for students and researchers interested in the future of biologically plausible neural modelling and its applications in medicine and artificial intelligence.