Erik C. Johnson

Neuromodulation in neural organoids with shell MEAs

The paper demonstrates neuromodulation in neural organoids using 3D shell MEAs, showing increased neuron firing rates with specific stimulation ranges.

Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory

This study demonstrates that human neural organoids exhibit features of learning and memory by showing synaptic plasticity, criticality, and receptor expression.

Modeling Organoid Population Electrophysiology Dynamics

This paper adapts CEBRA to generate low-dimensional embeddings of neural organoid electrophysiology data for analyzing activity changes and clustering bursting phenomena.

DeepPaint: A deep-learning package for Cell Painting Image Classification

DeepPaint is a deep learning package that classifies Cell Painting images without requiring cell segmentation, achieving high accuracy in binary and multiclass classification of biological perturbations.

Data-driven motif discovery in biological neural networks

This paper introduces a motif discovery method for biological networks that identifies statistically significant subgraphs from single network examples, addressing challenges in computational cost and statistical significance testing.

Informing generative replay for continual learning with long-term memory formation in the fruit fly

The paper introduces a computational model of generative replay in fruit fly mushroom bodies to improve continual learning, showing a 20% performance increase on CIFAR-100.

A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains

The paper predicts and matches observed negative interval correlations in neuronal spike trains using a stochastic dynamic threshold model with colored noise.

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

This paper develops a machine learning pipeline for detecting bacteria in electron microscopy images of brain tissue, demonstrating its potential for large-scale analysis and quality control.

Scalable graph analysis tools for the connectomics community

The paper introduces a software framework for comparative connectomics to analyze large-scale, diverse brain connectome datasets efficiently.

Online learning for orientation estimation during translation in an insect ring attractor network

The paper develops a neural model constrained by insect connectivity to perform sensor fusion and online learning for orientation estimation during translation, showing its viability in robotic systems.

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

DotMotif is an open-source tool that enables efficient subgraph isomorphism search and graph queries in connectomics data, demonstrated on simulated and real datasets.

A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

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

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity

This paper introduces a novel method using virtual agents and path-finding to efficiently identify neuron connectivity in electron microscopy images, achieving performance comparable to state-of-the-art methods at lower computational cost.

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

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