Dimitar Kostadinov

Dimitar Kostadinov

I am currently a postdoctoral fellow in Michael Häusser’s Neural Computation Laboratory in the Wolfson Institute for Biomedical Research at University College London. Over the last several years, I have been studying cerebellar computation during goal-directed behaviour using two-photon imaging, high-density electrophysiology, and optogenetics.

Prior to this, I was a PhD student in the Program in Neuroscience at Harvard University with Josh Sanes. I studied the mechanism and function of dendritic self-avoidance in the mammalian retina using a combination of mouse genetics, anatomy, and patch-clamp electrophysiology.

Currently reading: After the Fall by Ben Rhodes and Some Rain Must Fall: My Struggle 5 by Karl Ove Knausgård.

Currently listening to: Big Time by Angel Olsen, The Last Waltz by The Band.

Ongoing projects

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Cerebellar learning, fast and slow
Climbing fiber (CF) inputs to cerebellar Purkinje cells (PCs) encode sensorimotor and reward-related instructive signals that are utilized to learn and refine goal-directed behaviors. However, it remains unclear how these instructive signals evolve during the acquisition and refinement phases of learning, whether they target the same PCs, and whether different brain-wide pathways drive functionally distinct populations of CFs.
Dendritic gated networks: A rapid and efficient learning rule for biological neural circuits
In this project, we introduce a powerful new biologically plausible machine learning algorithm: the Dendritic Gated Network (DGN), a variant of the Gated Linear Network. DGNs combine dendritic ‘gating’ (whereby ‘interneurons’ target dendrites to shape ’neuronal’ responses) with local learning rules to yield provably robust performance.
Cerebellar cell type classification
We are combining cell type-specific optogenetics, electrophysiology, and machine learning to develop methods to classify cerebellar neurons based on their unique functional identities. This project is a collaboration between several cerebellar labs using high-density extracellular electrophysiology probes in multiple species (mouse, rat, and macaque).
Transformation of Purkinje cell population codes in the cerebellar nuclei
The interplay between Purkinje cells and nuclear cells has been a topic of debate in the cerebellar field for decades. While Purkinje cells are GABAergic, their effect on nuclear output may not be inhibitory, because nuclear neurons exhibit unique biophysical features that make them susceptible to entrainment by artificial stimulation of Purkinje cells.

Publications

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(2022). Reward signals in the cerebellum: Origins, targets, and functional implications. Neuron 110(8): 1290-1303.

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(2021). Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 372(6539) [16th of 39 authors].

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(2021). A rapid and efficient learning rule for biological neural circuits. bioRxiv.

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(2019). Predictive and reactive reward signals conveyed by climbing fiber inputs to cerebellar Purkinje cells. Nature Neuroscience 22(6): 950-62.

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(2019). Dynamics of the Inferior Olive Oscillator and Cerebellar Function. In: Manto M, Gruol D, Schmahmann J, Koibuchi N, Sillitoe R (eds) Handbook of the Cerebellum and Cerebellar Disorders. Springer, Cham.

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(2018). Combinatorial effects of alpha-and gamma-protocadherins on neuronal survival and dendritic self-avoidance. Journal of Neuroscience 38(11): 2713-29.

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(2017). Satb1 regulates contactin 5 to pattern dendrites of a mammalian retinal ganglion cell. Neuron 95(4): 869-83.

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(2012). Protocadherins mediate dendritic self-avoidance in the mammalian nervous system. Nature 488(7412): 517-21.

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