Moritz Helias

Recurrent dynamics underlying transient neural representations

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.

A mean-field toolbox for spiking neuronal network model analysis

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

Global organization of neuronal activity only requires unstructured local connectivity

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.

Strong and localized recurrence controls dimensionality of neural activity across brain areas

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 covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

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.

The Dynamics of Error Processing in the Human Brain as Reflected by High-Gamma Activity in Noninvasive and Intracranial EEG

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.