Social LEAP Estimates Animal Poses (SLEAP)

Posted on October 26th, 2020

SLEAP (Social LEAP Estimates Animal Poses) is a deep learning software framework for general purpose multi-animal limb tracking from video. This software couples a GUI for importing and annotating data with deep neural networks designed for learning to locate and associate user-specified anatomical landmarks on unmarked animals. Use cases range from kinematic studies of animal movement, to quantification of social dynamics via multi-animal part tracking.

Human Neocortical Neurosolver (HNN)

Posted on October 26th, 2020

Magneto- and electro-encephalography (MEG/EEG) are the two methods to non-invasively record human brain activity with millisecond temporal resolution. MEG and EEG provide reliable markers of healthy brain function and disease states. However, the difficulty of relating these macroscopic signals to the underlying cellular- and circuit-level neural generators is a major, fundamental limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate the findings into new therapies for neural pathologies. To address this problem, we built the Human Neocortical Neurosolver (HNN, https://hnn.brown.edu). HNN is a user-friendly software tool designed to help researchers, and clinicians interpret the cellular and network origins of MEG/EEG data. HNN’s core is a detailed, mechanistic neural model including canonical features of a layered neocortical circuit, with layer-specific thalamocortical and cortico-cortical drive. HNN’s model is uniquely designed to account for the biophysical origin of the electrical currents generating MEG/EEG with enough detail to connect to the underlying cellular-level activity. HNN provides a user-friendly graphical user interface so that researchers can work interactively between model and data without needing to alter the underlying mathematical model or the open-source code. Tutorials on how to simulate the most commonly measured signals, including event related potentials and brain rhythms (alpha, beta, gamma), are provided. Researchers can compare simulated signals to recorded data and easily manipulate the model parameters to develop and test alternative hypotheses for the neural origin of their signals. Micro-scale features, including layer-specific responses, cell spiking activity, and somatic voltages, can be visualized and used to guide validation of model predictions with a variety of invasive and non-invasive methods. HNN is being developed with best practices in open source software design and is also distributed with a python interface and corresponding python tutorials. The ability of HNN to associate signals across scales makes it a unique tool for translational neuroscience research.

Open Ephys GUI

Posted on October 26th, 2020

The Open Ephys GUI is open-source, plugin-based cross-platform software for acquiring data from implanted electrodes, used by hundreds of labs around the world. It is available for download from open-ephys.org/gui

cytoNet

Posted on October 25th, 2020

cytoNet provides a mathematical, web-based tool to rapidly characterize multiscale networks from images. To study complex tissue, cell and subcellular topologies, cytoNet integrates vision science with graph theory to quantify environmental effects on network topology. cytoNet applications include: (1) characterizing how pain sensation alters neural circuit activity in vivo, (2) quantifying patterns in how diverse brain cells respond to neurotrophic stimuli, & (3) uncovering cell cycle synchronization of differentiating neural stem cells. Awareness of cytoNet as a resource for the BRAIN Initiative community is a dissemination goal.

Toolkit for Oscillatory Real-Time Tracking and Estimation (TORTE)

Posted on October 25th, 2020

Please note this tool was previously referred to as “Phase Calculator Plugin”, which is one component of the Toolkit for Oscillatory Real-Time Tracking and Estimation (TORTE).

We have built a set of open-source tools for precisely locking experimental perturbations (optical/electrical/magnetic stimulation, behavioral triggering, etc.) to the phase of ongoing LFP oscillations (probably could be used with anything that has an oscillation, e.g. whisking). They are substantially more accurate than anything in the published literature and have been adapted to work with both human and animal experimental rigs. The target use case is understanding the role of oscillatory phase in cognition, e.g. altering phase relations between structures by timed perturbation.