Dimitar Kostadinov

The paper maps cerebellar functional connectivity in neonates, identifying intrinsic networks and cerebro-cerebellar clusters that mature with age, suggesting early foundations of adult organization.

This study investigates how climbing fiber signals mediate fast and slow learning in mice, finding distinct encoding patterns in different cerebellar regions across learning stages.

The paper maps monosynaptic and disynaptic connectivity of cerebellar lobules LV and LS, revealing segregated inputs and overlapping outputs that support both specialized and integrated functions.

This paper develops a deep-learning classifier to identify cell types from extracellular recordings across species with over 95% accuracy.

The paper introduces Neuropixels 2.0, a high-density probe for long-term brain recordings, and demonstrates its stability across months in multiple labs and species.

This paper introduces DGNs, a biologically plausible learning rule that outperforms backpropagation in efficiency and forgetting resistance, inspired by cerebellar structure and function.