Understanding how the brain computes across scales — from the firing of single neurons to the coordination of large-scale networks — remains one of the most critical and unsolved challenges in neuroscience. Traditional tools offer limited resolution, narrow coverage, or fragmented views of brain activity. In my group, we address this gap by engineering integrated lab-on-chip neuroelectronic platforms capable of capturing label-free, high-definition, large-scale neural dynamics with unmatched spatiotemporal precision. These platforms combine high-density CMOS-based electrophysiology with transcriptomic profiling and computational modeling, enabling us to record from thousands of neurons simultaneously, resolve multi-layer circuit interactions, and map functional activity directly onto molecular identity. For example, we can track how experience reshapes hippocampal network-wide connectivity, or how plasticity reorganizes the flow of information across the olfactory bulb — not just at the level of individual cells, but across the entire network.
This multiscale, multimodal capability opens the door to building more realistic computational models of brain function, identifying robust biomarkers, and uncovering the principles by which networks adapt, fail, and recover. In this talk, I will present how these innovations allow us to go beyond snapshots or isolated events and instead build a dynamic, systems-level understanding of neural computation — one that’s crucial for advancing both fundamental neuroscience and future neurotechnologies.

More information:

• https://www.dzne.de/en/research/research-areas/fundamental-research/research-groups/amin/research-areasfocus/