Computational Neuroscience

Nipoppy is a lightweight framework for standardized organization and processing of neuroimaging-clinical datasets. Its goal is to help users adopt the FAIR principles and improve the reproducibility of studies. The framework includes three components:

When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains.

A python package which aligns histology to the Allen Brain Atlas and Waxholm rat atlas using deep learning. Project Author(s) Harry Carey, Michael Pegios; Lewis Martin; Chris Saleeba; Anita J.

This repository contains the code of the Neuronal Spike Shapes (NSS), a simple approach for analyzing the electrophysiological profiles of cells based on their Action Potential (AP) waveforms. The NSS method explores the heterogeneity of cell types and states by summarizing the AP waveform into a triangular representation complemented by a set of derived electrophysiological (EP) features.

DataJoint Elements is a growing compilation of community-curated, open-source software modules for building automated data pipelines and analysis workflows for neuroscience experiments. DataJoint Elements enables: Secure tracking of animal subjects, equipment, and procedures Automatic data ingestion Customized research workflows Faster deployment Improved reproducibility Greater continuity across projects Secure tracking.

DataJoint Core is an open-source toolkit for defining and operating computational data pipelines (i.e., sequences of steps for data acquisition, processing, and transformation). Pipelines built in DataJoint Core offer: Efficient design with intuitive queries Automated, reproducible computation with full referential integrity Coordination of multiple human and computer workers Flexibility to adapt and change DataJoint Core includes libraries for Python and MATLAB, a REST API, and GUI tools for data entry and visualizations.

SqueakR is an open-source R package, available on CRAN, which streamlines bioacoustics research through automated data processing and visualizations for rodent vocalizations exported from DeepSqueak. These functions are harnessed through the ‘SqueakR’ Shiny Dashboard, available as a function within the package, which can be used to visualize experimental results and analyses, as well as conduct statistical significance test between call features across groups.

While accurate behavioral state classification is critical for many research applications, it is often done manually, which can be both tedious and inaccurate. Here we present a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity.

GeNN is a GPU enhanced Neuronal Network simulation environment supporting multiple frontends and backends. Spiking neural network models can be specified directly in Python or C++ or through interfaces to Brian 2, SpineCreator (SpineML), or PyNN.

This is a diffusion-weighted MRI processing Matlab toolbox (including binaries), which can be used to: • Compute the Q-Ball Imaging Orientation Distribution Function in Constant Solid Angle (CSA-ODF) (Aganj et al, MRM 2010).