Journal: Frontiers in Neuroscience

Article Title: Highly Flexible Precisely Braided Multielectrode Probes and Combinatorics for Future Neuroprostheses

doi: 10.3389/fnins.2019.00613

Figure Lengend Snippet: Simulation results. (A) Left hand side of the figure shows simulated site activity without noise added in blue, and independent components derived from noisy wire outputs in red. The four different spike shapes can be detected in this figure. On the right hand side of the figure, a site level collision is observed, and preserved by ICA. The activation scale and signal scale differ but the correlation coefficient of the reconstruction is 0.99. (B) DBSCAN method showing recovery of only clean non-colliding spikes. (C) Plexon Offline Sorter (POS) extracted spikes showing variation in spikes shapes caused by presence of spike collisions that are being assigned to spike clusters.

Article Snippet: To sort individual unit activity at each site, we used two approaches: (1) Density based spatial clustering of applications with noise (DBSCAN), a density based clustering algorithm first described in and implemented in the DBSCAN code on Matlab file exchange. (2) Plexon POS using K-Means or contour results as templates.

Techniques: Activity Assay, Derivative Assay, Activation Assay

Journal: Molecular Autism

Article Title: Beyond the three-chamber test: toward a multimodal and objective assessment of social behavior in rodents

doi: 10.1186/s13229-022-00521-6

Figure Lengend Snippet: Available programs used for USV analysis in rodents

Article Snippet: Mouse Ultrasonic Profile ExTraction (MUPET) , An open access MATLAB tool for data-driven analysis of USVs by measuring, learning, and comparing syllable types MUPET uses an automated and unsupervised algorithmic approach for the detection and clustering of syllable types summarized in the following features: Syllable detection by isolating and measuring spectro-temporal syllable variables, followed by analyzing overall vocalization features (syllable number, rate and duration, spectral density, and fundamental frequency) The application of unsupervised machine learning based on k-means clustering to build “syllable repertoire” from the dataset which includes up to several hundreds of the most represented syllable types based on spectral shape similarities within that dataset Similarity measurement between syllable types of two different repertoires using rank order comparisons in a manner that is frequency-independent Centroid-based (k-medoids) cluster analysis of syllable types from different syllable repertoires of different datasets to measure the frequency of use of different syllable types across conditions or strains and identify shared and unique shapes Provides automated time-stamps of syllable events for synchronized analysis with behavior The option for the user to control features regarding noise reduction, minimum and maximum syllable duration, minimum total and peak syllable energy, and the minimum inter-syllable interval needed to separate rapidly successive notes into distinct syllables Cannot detect USVs below 30 kHz [ ] , [ ] .

Techniques: Extraction, Software, Control