David Dahmen

The paper investigates how task engagement affects interareal communication geometry in the mouse visual thalamocortical-midbrain circuit, showing reduced network activity and dimensionality.

This paper introduces a framework for modeling metastable neural assemblies by varying a mixing parameter between structural and synaptic clustering, showing its impact on network dynamics and implementation trade-offs.

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 evaluates the kernel method for predicting electric brain signals from population firing rates, showing its accuracy depends on kernel heterogeneity and spike-train correlations.

The paper shows that topographic modularity in neural circuits improves signal-to-noise ratio and task performance through recurrent inhibition.

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 analyzes resting state spiking activity in monkey motor cortex, finding higher dimensionality, lower firing rates, and less excitation-inhibition balance compared to task-related states.

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.