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MY RESEARCH INTERESTS

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Recurrent circuits and the role of distinct inhibitory populations

In my research, I use computational models and mathematical tools to study neuroscientific questions. I have used rate-based and spiking-based models of either feedforward or recurrently connected networks of excitatory and inhibitory neurons. In addition, I have analyzed calcium imaging and voltage imaging datasets.

Recently, I have become specifically interested in investigating the roles different inhibitory neuron types play in cortical computations.

I am very interested in the phenomena of synaptic plasticity. The change of synaptic strength, i.e. synaptic plasticity, has long been proposed to be the underlying mechanism of complex cognitive phenomena like learning and memory formation.

In my work, I have focused specifically on plasticity at inhibitory synapses, and I have studied the functional consequences of this inhibitory plasticity in different contexts.

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Adapted from Miehl & Gjorgjieva, 2022 (PLoS CB)

Synaptic Plasticity

Neuro-inspired AI

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Adapted from Onasch*, Miehl* et al., 2025 (bioRxiv).

I am interested in bridging between Neuroscience and AI by using biological principles (e.g. dendrites, inhibitory subtypes, neuromodulation) to improve deep learning and reinforcement learning algorithms.

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