Eva L. Dyer

This study shows that mice improve prey capture by reorganizing search strategies in complex environments, using memory and learned object values.

The paper investigates how odor information is routed through feedforward and feedback pathways in the mouse olfactory cortex, finding distinct temporal encoding and plasticity in neurons projecting to different targets.

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

The paper identifies a high-frequency, millisecond-scale brain state embedding that persists across sleep and wake states and correlates with behavior.

This study shows neurons encode their anatomical location in spike trains, which can be decoded across brain regions and conditions.

This study shows that tauopathy disrupts network-level homeostatic set-points in hippocampal activity but not individual neuron activity, with implications for computational capacity and behavior.

The paper investigates whether transcriptomic cell type can be inferred from neuronal activity timing across multiple time scales using deep learning.

The paper investigates circuit-specific vulnerability in the default mode network (DMN) in Alzheimer's disease, finding that dysfunction is restricted to in-DMN projecting circuits despite widespread amyloid burden.

This paper introduces a transformer architecture that models both individual and collective dynamics in time-varying systems, demonstrating its effectiveness on neural activity data.

This paper introduces Swap-VAE, an unsupervised method for learning disentangled neural activity representations using self-supervised learning on neural recordings.

The paper provides a three-dimensional X-ray microtomography dataset of thalamocortical structures to study neural heterogeneity.

This paper introduces a deep learning approach to map brain microarchitecture by learning features from local image patches to identify anatomical regions and subdivisions.

The paper uses a network diffusion model to study multi-sensory integration in the mouse brain, finding that specific regions like the claustrum and parietal temporal cortex play central roles in integrating sensory information.

This paper uses a deep generative model to analyze brain structure variability across mouse brains and validates the model through structured perturbations.

The paper develops a scalable, reproducible framework for processing large neuroimaging datasets using open-source tools and standardized pipelines.

This paper introduces automated methods for detecting cellular layers in brain histology images, showing their feasibility on mouse brain and retina data.

This paper introduces a new decoding method called distribution alignment decoding (DAD) that uses movement statistics to map neural activity to motor variables, achieving comparable or better performance than traditional supervised decoders in some cases.