Sonja B. Hofer

Zapit provides an open-source platform for random-access laser-scanning optogenetics in mice, enabling user-friendly and accessible experiments.

The paper introduces BakingTray, an open-source software platform for block-face serial two-photon tomography, demonstrating its utility in neuroscience applications.

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

The paper identifies a neural pathway involving plHVAs and vLGN that enables learning to suppress fear responses through endocannabinoid-mediated plasticity.

This study shows that reciprocal excitatory connections between cortical areas create line attractor dynamics that promote consensus on stimulus features across areas.

The paper identifies a thalamocortical disinhibitory circuit in mouse V1 that selectively amplifies unexpected visual stimuli, implementing sensory prediction errors through VIP and SOM interneuron interactions.

The paper shows that inhibitory neurons in mouse primary visual cortex selectively connect with excitatory neurons based on response similarity, stabilizing network dynamics and enabling subnetwork competition.

The paper identifies the median raphe nucleus as a neural hub that flexibly controls behavioral strategies like exploitation, exploration, and disengagement through distinct neuronal populations.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

The paper shows that learning realigns cortical network dynamics to better integrate relevant sensory input in mice, explained by changes in excitatory-inhibitory connectivity.

The paper quantifies dynamic causal interactions between neocortical areas using electrophysiological recordings and perturbations, finding time-varying influence patterns in visual cortex.

The paper introduces a database and deep learning tool for spike inference from calcium imaging data, outperforming existing methods with an automated, parameter-free algorithm.

This paper develops a standardized task for measuring decision-making in mice, showing reproducible results across multiple laboratories and providing a public dataset and tools.

The paper shows that transthalamic pathways in mice carry distinct visual information to higher cortical areas compared to direct intracortical projections.