Moritz Helias

The paper identifies three computational characteristics of neural circuits in mice that drive reliable stimulus representations and reveals a trade-off in coding space when increasing the number of stimuli.

The paper introduces an open-source Python toolbox for analyzing spiking neuronal networks using mean-field methods, enabling estimation of network properties without simulation.

The paper shows that heterogeneous local connectivity enables complex long-range coordination in neuronal networks, explaining covariance patterns in motor cortex during rest and task.

The paper shows that recurrent synaptic networks in the mouse cortex strongly control neural activity dimensionality across time and brain areas, mediated by cell-type specific network motifs present in both mouse and human brains.

The paper introduces a new classification framework for time series data using covariance patterns in recurrent neuronal networks, showing it can effectively capture second-order statistical features.

This study investigates error processing in the human brain using high-gamma activity from noninvasive and intracranial EEG, revealing distinct global and local dynamics in error-related responses.