Anna Levina

The paper develops a framework to analyze burst shapes in neuronal cultures, showing they provide more detailed information about network dynamics than traditional features.

The paper analyzes intrinsic neural timescales across the entire mouse brain, revealing spatial patterns and a multiscale architecture of timescale diversity.

This study shows that structured presynaptic connectivity can enable co-tuning of excitation and inhibition in neurons despite noisy, correlated inputs.

This study shows that neural networks optimized for efficient coding exhibit criticality signatures near optimal noise levels, linking efficient coding and criticality theories.

The paper develops a dynamical system model to characterize in vitro neuronal network bursting dynamics across development and cell types, finding that effective excitability reliably captures these differences.

The paper measures intrinsic timescales in the visual cortex during attention tasks and finds they change with attention and spatial connectivity.

This paper introduces a Bayesian framework for estimating timescales from dynamic processes, showing it outperforms standard methods on synthetic and primate cortex data.

The paper introduces new neuronal models that incorporate energy and spike constraints to better capture complex activity patterns and metabolic disruptions in neurological disorders.