A color-based visualization technique for multi-electrode spike trains
Multi-electrode recordings of neuronal activity provide an overwhelming amount of data that is often difficult to analyze and interpret. Although various methods exist for treating multi-electrode datasets quantitatively there is a particularly prominent lack of techniques that enable a quick visual exploration of such datasets. Here, by using Kohonen self-organizing maps, we propose a simple technique that allows for the representation of multiple spike-trains through a sequence of color coded population activity vectors. When multiple color sequences are grouped according to a certain criterion, e.g. by stimulation condition or recording time, one can inspect an entire dataset visually and extract quickly information about the identity, stimulus-locking and temporal distribution of multi-neuron activity patterns. Color sequences can be computed on various timescales revealing different aspects of the temporal dynamics and can emphasize high-order correlation patterns that are not detectable with pairwise techniques. Furthermore, this technique is useful for determining the stability of neuronal responses during a recording session. Due to its simplicity and reliance on perceptual grouping, the method is useful for both quick online visualization of incoming data and for more detailed post-hoc analyses.
Free source code for creating color sequences from spike trains. Matlab code coming soon.