Merav Stern

The paper develops and evaluates four deconvolution methods to recover neuronal spiking rates from wide-field calcium imaging data, showing they outperform existing methods and highlight the importance of accurate temporal deconvolution.

This paper presents an analytic framework to infer population dynamics from wide-field calcium fluorescence data, showing improved accuracy and efficiency over existing methods.

This paper shows random neural networks with self-couplings generate multiple timescales of activity, enabling spectral demixing through cluster-specific entrainment to input components.

The paper shows that flow conditions during odor plume exposure affect the temporal representation of odor in the mouse olfactory bulb.

The paper develops and evaluates methods to estimate neural spiking rates from wide-field calcium imaging data by deconvolving calcium indicator dynamics and accounting for noise.

This paper demonstrates a single spiking neuron model that can learn to estimate numerosity and perform counting tasks with high precision, outperforming convolutional neural networks in some scenarios.

The paper models how piriform cortex transforms temporal latency coding from the olfactory bulb into ensemble coding, showing robust odor representation across concentration changes.