neurotic: Neuroscience Tool for Interactive Characterization

An example configuration file. To visualize experimental data with neurotic, datasets must be listed and described within human-readable plain text YAML configuration files. Multiple configurations for the same or different datasets may be listed within one file and are separated into blocks using blank lines and indentation. This example shows one configuration that is similar to that used to create Figures 1, 3. Each set of parameters shown is optional. A, Each configuration is given a unique name, e.g., “feeding experiment.” Details associated with the configuration are indented beneath the name. A brief description of the dataset may be provided. The names and descriptions of datasets are displayed when a YAML file is loaded, giving the user the option to select the dataset to view (Fig. 1B). B, Paths to related files found locally on the computer, e.g., signals and video from a single experiment, may be located within a single directory specified by data_dir (such a file tree structure is convenient but not necessary as neurotic can accept relative or absolute paths). A file containing signal data that is readable by the Python package Neo (Garcia et al., 2014) is specified using data_file. Neo supports many file types (see list: https://neo.readthedocs.io/en/latest/io.html#module-neo.io). Signals are read from the data file, processed according to other, optional configuration settings, and plotted. For example, in this figure, sections D, E, G affect plotting. C, A video file may be associated with the signal data. Synchronization parameters can be given for controlling the video and signal data alignment. For example, in this case, video capture began 2.51 s before signal data acquisition, so −2.51 is provided for video_offset to shift the video start time. Parameters for correcting for frame rate inaccuracies or clipping the video are also available. D, All signals or a subset of signals will be plotted according to the parameters given under plots, which control plot range and labeling (Fig. 1A, top left panel). E, Signals may be filtered before plotting. F, An optional epoch encoder GUI panel (Fig. 1D) creates annotations that may be saved to a spreadsheet (CSV) file. The epoch encoder allows user-defined labels to be attached to time periods. G, Peaks may be identified in the signals using amplitude thresholds. Each amplitude discriminator is applied to the specified signal channel and may be constrained to periods marked with a particular epoch label. This creates one spike train for each amplitude discriminator, plotted both as points on the signals and as a raster plot (Fig. 1C). See Figure 3 for algorithm details. H, Bursts of activity in spike trains may be detected using initiation and termination firing frequencies.

Peak detection using a window discriminator to define neuronal bursts. Panels show an example of applying three amplitude discriminators to a single channel to obtain three sets of peaks, classified as action potentials originating from identified motor neurons B38 (McManus et al., 2014), B6/B9, and B3 (Lu et al., 2015). The configuration settings are shown in Figure 2G. A, The raw signal obtained from an extracellular whole nerve recording of BN2 is plotted and contains activity from motor and sensory neurons. Identified neurons are distinguishable by their relative amplitude and timing within the feeding motor pattern (Lu et al., 2013). B, Epochs labeled “B38 activity” and “B3/B6/B9 activity” were placed manually using the epoch encoder (Fig. 1D) based on prior work. Amplitude windows for spike classes were estimated manually and specified in the configuration file. C, At load time, using algorithms from the Python package elephant (http://pypi.org/project/elephant), local maxima or minima were located within the given amplitude range and contained within a period with an appropriate epoch label. These points are displayed on the signals and as raster plots (Fig. 1C). D, Subsets of each spike train were grouped into bursts defined by firing frequency thresholds (boxes). Notice that the initial B38 spike and the final B3 spike were excluded from the bursts because they did not meet the firing frequency conditions. Burst times may then be used for further analysis.

Using neurotic to explore an adaptive response to load during feeding. A, As an animal swallowed a strip of unloaded (unanchored) seaweed, activity of identified feeding motor neurons B6/B9 and B3 was identified from the extracellular nerve recording of BN2. Using the synchronized video and epoch encoder, the timing of inward movement of the strip and the approximate period during which the identified neurons fired were labeled (gray and blue bars, respectively). Using the peak detection feature, medium-sized spikes were classified as B6/B9, and large spikes were classified as B3, which are major motor neurons known to contribute substantially to swallowing force (Lu et al., 2015). Spikes are plotted as points and in raster plots. B, In a separate trial, the same animal fed on a strip of unbreakable seaweed anchored to a force transducer. The force generated by the animal is plotted with the extracellular nerve signals. In response to the increased load, the animal activated the identified motor neurons at high frequency for longer, leading to greater force. C, Using firing frequency criteria determined by prior work (Lu et al., 2015), all spikes generated when feeding on an unloaded seaweed strip associated with the B6/B9 motor neurons (light blue) were grouped into a burst. The two spikes generated by B3 (dark blue) did not meet the criterion. D, Using the same criteria when the animal fed on a loaded strip, all B6/B9 spikes and all but one B3 spike were grouped into bursts. The data suggest that the B3 neuron is more likely to activate at high frequency when the animal encounters load, and the B6/B9 neurons are active for a longer duration (Gill and Chiel, 2020).