Michael A. Buice

Brain-wide reconfiguration of burst firing by psilocybin reveals 5-HT2A-dependent circuit dynamics

In awake mice, psilocybin changed burst firing rather than mean firing rate across thalamus and hippocampus. Ketanserin pretreatment abolished most burst effects, but EEG power and firing-rate changes persisted.

Psilocybin collapses visual change detection and drives cortical dynamics toward a state of surprise

This study shows psilocybin disrupts visual change detection by altering cortical dynamics, particularly in SST interneurons, leading to increased surprise signals.

Hebb's Vision: The Structural Underpinnings of Hebbian Assemblies

The paper identifies neural assemblies consistent with Hebb's theory using a novel dataset linking optical physiology to connectivity, finding they are structurally integrated but not defined by strong monosynaptic connections.

Deciphering neuronal variability across states reveals dynamic sensory encoding

The paper analyzes neuronal variability across brain states to understand how factors like internal dynamics, behavior, and stimuli influence sensory processing over time.

Bounds on the computational complexity of neurons due to dendritic morphology

This paper examines the computational limits of neurons with complex dendritic structures, showing that their capacity exceeds simple linear units and varies with morphological architecture.

Spatial organization of multisensory convergence in mouse isocortex

The paper examines spatial convergence patterns of sensory inputs in the mouse isocortex, introducing the topography dimension (TD) and its relationship with neural activity and hierarchy.

Complex Properties of Training Stimuli Affect Brain Alignment in a Deep Network Model of Mouse Visual Cortex

This study investigates how training data properties affect the alignment of deep neural networks with mouse visual cortex activity, finding that naturalistic environments significantly improve similarity.

Modeling functional cell types in spike train data

The paper develops a method to simultaneously fit single-cell neural activity models and group neurons into functional cell types, improving prediction accuracy and interpretability.

Saccade-responsive visual cortical neurons do not exhibit distinct visual response properties

The paper analyzes saccade-responsive neurons in visual cortex, finding their distribution varies by cell type, area, and layer, but they do not show distinct visual response properties.

Stimulus novelty uncovers coding diversity in survey of visual cortex

The paper identifies diverse functional neuron types in the visual cortex that process novel and familiar stimuli in mice.

CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex

The paper introduces MouseNet, a biologically constrained CNN model of the mouse visual cortex, and shows it achieves 2/3rds of VGG16's ImageNet performance while recapitulating neural representations.

Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology

This study compares neuronal responses in visual cortex using electrophysiology and two-photon imaging in awake mice, finding differences in responsiveness and selectivity and exploring data transformations to reconcile them.

Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex

The paper benchmarks deep neural networks on mouse visual cortex data to assess their ability to model neural activity and resolve discrepancies in previous findings.

Relationship between simultaneously recorded spiking activity and fluorescence signal in GCaMP6 transgenic mice

The paper characterizes the relationship between spiking activity and fluorescence signals in GCaMP6-expressing neurons, finding GCaMP6s has better single-spike detection than GCaMP6f.

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

The paper introduces a convolutional neural network model of the mouse visual cortex constrained by neuroanatomical data and provides code for research.

Flexible neural connectivity under constraints on total connection strength

The paper models neural connectivity under constraints on total synaptic weight and finds that fixed total connection strength better explains Kenyon cell connectivity in Drosophila mushroom bodies than other models.

Experience shapes activity dynamics and stimulus coding of VIP inhibitory and excitatory cells in visual cortex

The paper examines how visual experience affects activity dynamics and stimulus coding in excitatory and VIP inhibitory cells in mouse visual cortex during a change detection task.

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

The paper presents a large-scale dataset of neural activity in the mouse visual cortex and shows that responses to different stimuli are largely independent, suggesting multi-layer hierarchical computation.

VIP interneurons selectively enhance weak but behaviorally-relevant stimuli.

The paper shows VIP interneurons in mouse visual cortex have complementary contrast tuning to SST neurons and synergistically respond to visual motion and locomotion, enhancing behaviorally-relevant stimuli while maintaining network stability.

Transformation of population code from dLGN to V1 facilitates linear decoding

The paper investigates how neural representations transform from dLGN to V1 in mice, finding evidence for both sparse coding and linear separability.

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging.

This study calibrates two-photon calcium imaging to recover action potentials in mice, finding that fluorescence changes are inconsistent for small spike numbers and vary by mouse line.

Dimensionality in recurrent spiking networks: global trends in activity and local origins in connectivity

This paper investigates how dimensionality in spiking neural networks is influenced by connectivity and stimulus input, revealing principles linking local connectivity to global activity patterns.

Visual physiology of the Layer 4 cortical circuit in silico

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.

Predicting how and when hidden neurons skew measured synaptic interactions

The paper develops a framework to quantify how hidden neurons affect measured synaptic interactions in neural circuits.

Mouse hue and wavelength-specific luminance contrast sensitivity are non-uniform across visual space

The paper investigates how mice perceive hue and wavelength-specific luminance contrast across visual space, finding non-uniform sensitivity patterns.

From the statistics of connectivity to the statistics of spike times in neuronal networks

The paper links network connectivity statistics to spike time statistics in neuronal networks, showing how local connectivity features predict global activity patterns and how plasticity rules can coevolve connectivity and spiking statistics.

Linking structure and activity in nonlinear spiking networks

This paper develops a framework to link neural connectivity and activity in nonlinear spiking networks, showing how structure and nonlinearities shape population activity.