Human Neocortical Neurosolver
HNN is designed to be a user-friendly software tool for researchers and clinicians to develop and test hypotheses on the neural origins of their EEG/MEG data. The purpose is to bridge the “macroscale” extracranial recordings to the underlying cellular- and circuit- level activity.
How does it work?
HNN’s underlying neural model simulates the primary electrical currents in the neocortex that create EEG/MEG signals. The model simulations are based on the biophysical origins of the primary electrical currents.
What can it do?
The HNN software enables visualization of primary electrical currents (i.e., equivalent current dipoles) (nAm), individual cell spiking activity, and calculation of frequency domain oscillations (time-frequency analysis and power spectral density). HNN is intended for studying circuit activity from one, or (coming soon), several, source localized regions of interest based on the local network dynamics, and the thalamo-cortical and cortical-cortical inputs that contribute to the local activity.
Where can I learn more?
To learn more, please visit our software webpage at http://hnn.brown.edu
On our website, you will find the following:
Overview of the software and its capabilities
Details on the biophysics of the underlying model
Links to install the software on your local operating system
Tutorials that will guide you through the GUI and get you started with using HNN by walking through a series of examples
What is it?
The SpectralEvents toolbox is composed of a series of Matlab functions that find and analyze spectral events on a trial-by-trial basis, defining them as local maxima in the spectrogram above a power threshold and within a specified band of the non-averaged time-frequency response (Shin et al. eLIFE 2017). While the spectral features of multiple time series trials may appear to show continuous, rhythmic activity in the average time-frequency response, we have often found spectral features in a given trial to instead be transient. By characterizing the transient features of spectral activity on a trial-by-trial basis, the SpectralEvents toolbox strives to reduce information loss due to spectrogram averaging and uncover relevant time-frequency correlations with experimental outcomes.
Where can I learn more?
Please see our GitHub page (jonescompneurolab/SpectralEvents) for more information on the SpectralEvents toolbox.