Kyra Kadhim

Kyra Kadhim
Kyra Kadhim studies how the brain processes visual information, starting from the very first synapses in the retina. In the past, she worked on pulse-coupled oscillator network dynamics and mechanistic models of the cortex. Her current work aims to build a strong foundation for our understanding of higher cortical functions. She is also interested in how the neural implementation of visual information processing can inform artificial neural network architecture, and how machine learning techniques for parameter optimization and model discovery can be leveraged to build useful mechanistic models.
I enjoy working with people from all different backgrounds and walks of life to tackle fundamental problems in computational neuroscience such as how visual information is processed in biological systems.
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Deistler, M., Kadhim, K. L., Pals, M., Beck, J., Huang, Z., Gloeckler, M., ... & Macke, J. H.

Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.

Nov 13, 2025 | Nature Methods, 1-9
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Beck, J., Bosch, N., Deistler, M., Kadhim, K.L., Macke, J. H., Hennig, P., Berens, P.

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

Jun 25, 2024 | International Conference on Machine Learning 2024