Christian K. Machens

The paper examines how feedforward and feedback pathways between mouse V1 and PM modulate visual responses, showing distinct functional roles in recurrent cortical loops.

The paper develops a behavioural assay to study flexible navigation in mice, showing they learn maze structures quickly and navigate efficiently.

The paper uses recurrent network models to show how input alignment with connectivity shapes directional interactions in cortical circuits, revealing excitatory inputs' dominant role in controlling directionality.

This study investigates how midbrain dopamine neurons control male mouse copulatory behavior, finding that their activity is experience-independent and critical for sustaining thrusting actions during mating.

The paper presents a framework to understand hippocampal remapping through three mechanisms using a linear decoder and population geometry perspective.

This paper investigates how the geometry of neural activity in recurrent networks depends on connectivity structure and input dimensionality, revealing how activity subspace dimensions relate to connectivity rank and input configuration.

The paper shows that representational drift can occur through changes in neuronal excitability rather than synaptic plasticity, maintaining stable decoding and behavior.

Megabouts is a software package for zebrafish locomotion analysis that uses a Transformer neural network to classify behaviors flexibly across tracking methods and frame rates.

The paper uses dimensionality reduction to identify distinct populations of neuronal activity in the zebrafish hindbrain associated with translation and rotation behaviors.

This paper introduces DLAG, a framework to disentangle bidirectional signal flow between neuronal populations, demonstrating its effectiveness on synthetic data and primate visual areas.

The paper explains how spiking neural networks maintain robustness through low-dimensional representations and bounding box integrity under parameter changes.

The paper examines feedforward and feedback interactions between visual cortical areas and finds they use distinct population activity patterns.

This study challenges conventional models of decision variable representation in the parietal cortex by showing opposite response patterns in a face-discrimination task compared to motion-discrimination tasks.

The paper investigates how reward expectations are influenced by internal representations in rodents, finding that efficient, compressed representations best match both behavior and dopaminergic activity.

The paper investigates how brain state affects the geometry of population activity in rat auditory cortex using sound stimuli and finds state-dependent changes in coding properties and noise structure.

This study shows that striatal neuron dynamics predict and are necessary for duration judgments in rats performing a psychophysical task.