I study the computational principles underlying information processing in neural circuits. Using efficient coding as a theoretical framework, I investigate how recurrent connectivity and structured excitation and inhibition shape neural dynamics and population computations. Much of my work so far has focused on how sensory information is encoded, transmitted, and transformed across neural circuits. I am a postdoctoral researcher at the Institute for Neural Information Processing at the University Medical Center Hamburg-Eppendorf.
Featured Research
Structured recurrent inhibition computes contrast across converging somatosensory signals
Koren V, Emanuel AJ, Panzeri S. Research manuscript in preparation.
We investigate how structured recurrent inhibition enables neural circuits to compute contrast across converging somatosensory inputs.
Talk — SNUFA 2024 · COSYNE 2025 poster, abstract and video
Efficient coding in biophysically realistic excitatory-inhibitory spiking networks
Koren V, Blanco Malerba S, Schwalger T, Panzeri S. eLife, 2025.
We show how efficient coding accounts for structured excitatory-inhibitory connectivity and biologically realistic coding, dynamical and biophysical properties of cortical networks.
Paper · Talk — SNUFA 2023 · Repository
Biologically plausible solutions for spiking networks with efficient coding
Koren V, Panzeri S. Advances in Neural Information Processing Systems (NeurIPS), 2022.
We extend the theory of efficient coding to spiking networks with biologically plausible excitatory-inhibitory architectures, intrinsic currents, and structured recurrent connectivity.
Paper
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About me
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