Cyriel M. A. Pennartz

The paper identifies AC-to-V1 projecting neurons in mice and shows they have distinct audiovisual responses independent of motor activity.

Neuroline integrates multimodal neurophysiology data into a standardized format, enabling FAIR data sharing and cross-modal analysis.

The paper shows that visual cortical population micro-states are organized by behavioral arousal rather than discrete sleep-wake states in mice.

The paper investigates how mouse visual cortex neurons respond to binocular stimuli and finds that only binocular cells show specific integration of congruent stimuli.

This study examines how binocular conflict affects visual cortex responses in mice, finding that stimulus laterality influences suppression and recovery under anesthesia.

The paper investigates how sound-evoked orofacial motion affects visually responsive V1 neurons during a multisensory detection task with delayed reward, finding that motor-related activity only impacts a fraction of these neurons.

The paper shows that baseline activity of V1 interneurons mediates the relationship between pupil-linked arousal and engaged behavioral state in mice and humans.

The paper introduces a computational model integrating cortical and subcortical processing to better replicate brain behavior in decision-making tasks.

The paper shows that perceptual reliability reduces membrane potential variability in cortical neurons during task-relevant stimuli.

This study uses recurrent neural networks to model multisensory decision-making and finds that behavioral variability arises from network-specific strategies and modality-selectivity.

This study shows that higher-order thalamic nuclei show strong correlations in activity across awake and anesthetized mice, suggesting shared synaptic inputs and challenging the classical view of isolated nuclei.

The paper presents a computational model of predictive coding in the cortex that incorporates biologically plausible neuron types and connectivity, showing how they generate oscillatory patterns and handle prediction errors.

This study shows that mouse V1 neurons switch orientation preference between standard and deviant visual stimuli, revealing context-dependent flexibility in predictive coding.

The paper shows that synaptic plasticity, not intrinsic circuit structure, enables oscillations in a mouse V1 cortical column model with multiple interneuron types.

The paper develops a spiking network model of a mouse V1 cortical column to study how feedforward and feedback inputs affect neural dynamics under different network states.

This study uses a biologically detailed model of the mouse V1 cortical column to explain orientation selectivity through laminar-distributed interactions and subnetwork coupling.

The paper develops a computational model that distinguishes self-generated from externally caused optic flow using sensorimotor mismatch circuits, validated with mouse data and applicable to primate vision.

The paper analyzes neural activity in mouse cortex during stimulus detection and develops a network model showing PPC mediates report-related activity in V1, with PFC playing a secondary role.

The paper investigates hippocampal place cell sequences during a visual discrimination task and finds they recapitulate paths near the chosen reward site, independent of perirhinal cortex activity.

This paper develops a spiking neural network for predictive coding that generates hierarchical internal representations and reconstructs unseen data, suggesting biologically plausible mechanisms for perceptual inference.

This study shows that acute bilateral DREADD inhibition of the dorsal dentate gyrus in rats impairs initial learning of similar place-reward associations but not reward sensitivity.

This paper shows how a predictive coding network learns invariant object representations and generative models through local prediction error minimization, using dynamic inputs to simulate visual perception.

The paper shows that spiking activity in auditory and visual cortex of rats coherently represents spatial position and head direction during a sensory detection task.

The paper examines how pre-stimulus arousal variability affects post-stimulus neural activity in ferret visual and parietal cortices during quiet wakefulness.

The paper dissociates auditory, motor, and visual processing in V1 of awake mice, showing distinct spatiotemporal profiles and segregated neuronal populations.

The paper shows that task complexity extends the time window in which V1 is necessary for perceptual decisions, overlapping with report-related activity and affecting behavioral performance.

The paper develops a deep predictive coding network trained with Hebbian learning that exhibits neural response properties similar to experimental findings in the visual cortex.

This study shows that inactivation of two higher-order visual areas in mice broadens stimulus responsiveness in primary visual cortex by amplifying responses to non-preferred stimuli.

The paper examines how information flow between brain regions changes during non-REM sleep in rats, finding that task-related neurons show increased short-time communication while non-task neurons show increased long-time communication.