Eric Shea‐Brown

Temporal Deconvolution of Mesoscale Recordings

The paper develops and evaluates four deconvolution methods to recover neuronal spiking rates from wide-field calcium imaging data, showing they outperform existing methods and highlight the importance of accurate temporal deconvolution.

Global and local origins of trial-to-trial spike count variability in visual cortex

The paper investigates trial-to-trial variability in visual cortex spike counts and finds that global cortical activity explains more variability than behavioral factors.

Data Heterogeneity Limits the Scaling Effect of Pretraining in Neural Data Transformers

This study shows that data heterogeneity limits the scaling benefits of pretraining neural data transformers in neuroscience datasets, suggesting that careful session selection can improve performance.

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

The paper shows that chaotic dynamics in recurrent neural networks separate learning regimes and correlate with a consciousness measure, suggesting a link between critical dynamics, learning, and consciousness.

A biologically inspired architecture with switching units can learn to generalize across backgrounds

The paper introduces a bio-inspired neural network architecture with switching units that improves generalization across different visual backgrounds.

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.

Heterogeneity in Neuronal Dynamics is Learned by Gradient Descent for Temporal Processing Tasks

The paper develops a neuronal model with complex dynamics and shows it can learn temporal tasks using gradient descent, exhibiting diversity and robustness.

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.

A scale-dependent measure of system dimensionality

The paper introduces a scale-dependent measure of system dimensionality and applies it to neural recordings, showing differences in effective dimensionality between behaviorally engaged and spontaneous brain states.

Recurrent neural networks learn robust representations by dynamically balancing compression and expansion

This study shows how recurrent neural networks balance compression and expansion of activity dimensionality during learning to solve discrimination tasks effectively.

Cortical network structure mediates response to stimulation: An optogenetic study in non-human primate cortex

This study shows that pre-existing cortical network structure, rather than stimulation protocol alone, primarily determines functional reorganization induced by optogenetic stimulation in non-human primates.

A solution to temporal credit assignment using cell-type-specific modulatory signals

This paper proposes a learning rule for recurrent spiking neural networks that incorporates cell-type-specific modulatory signals to improve temporal credit assignment.

Autoencoder networks extract latent variables and encode these variables in their connectomes

The paper uses autoencoder networks to show that regularization can resolve ambiguity in connectivity weights, allowing latent variable extraction through nonlinear dimensionality reduction.

Identification of multiple noise sources improves estimation of neural responses across stimulus conditions

The paper introduces a modeling framework that incorporates multiple noise sources to better estimate neural responses across stimulus conditions, revealing light level-dependent changes in retinal ganglion cell responses.

Learning to Predict in Networks with Heterogeneous and Dynamic Synapses

The paper shows that recurrent neural networks with short-term synaptic plasticity outperform standard models in predictive tasks by leveraging learned synaptic time constants and activity history encoding.

Predictive learning as a network mechanism forextracting low-dimensional latent spacerepresentations

This study investigates how predictive learning in neural networks generates low-dimensional latent representations of sensory inputs by analyzing network dynamics and intrinsic dimensionality.

Nonlinear convergence boosts information coding in circuits with parallel outputs

The paper shows that convergent circuits with nonlinear subunits and divergent parallel pathways can encode more information than linear subunits, despite individual losses from convergence and nonlinearities.

Cortical representation variability aligns with in-class variances and can help one-shot learning

The paper shows that cortical representations of visual stimuli align with in-class variability, enabling one-shot learning through noise-driven generalization.

Strong and localized recurrence controls dimensionality of neural activity across brain areas

The paper shows that recurrent synaptic networks in the mouse cortex strongly control neural activity dimensionality across time and brain areas, mediated by cell-type specific network motifs present in both mouse and human brains.

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.

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

The paper demonstrates a neural circuit in V1 that can switch between static and moving visual contexts using a VIP population as a switch, achieving efficient context-dependent processing.

Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging

The paper develops and evaluates methods to estimate neural spiking rates from wide-field calcium imaging data by deconvolving calcium indicator dynamics and accounting for noise.

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.

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.

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.

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.

High resolution data-driven model of the mouse connectome

The paper constructs a high-resolution voxel-scale model of the mouse connectome using anterograde tracing data, outperforming previous regional models.

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

This paper introduces a maximum entropy model that captures higher-order neural interactions by adaptively identifying estimable moments, improving prediction of spiking pattern frequencies.

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.