Electrophysiological recordings of single neuron activity are the fundamental tool for investigating brain function. Since a recording electrode often detects spikes from more than one neuron, a spike sorting technique is needed to identify and separate spikes emitted from different neurons.
Most currently available computational procedures provide accurate sorting and classification, but are often highly interactive and time-consuming and require specific experience and subjective judgments. Fast automatic methods are available, but they usually not as accurate as the offline ones and often suffer from problems such as false match or double match, spike overlap and errors in classification. Solving the tradeoff between automation, speed and accuracy of spike sorting is a current important challenge in systems neuroscience, which is crucial for improving our ability to analyze large simultaneously recorded populations and thus understand the nature of the neural population code.
Here, we aim at contributing to the progress in achieving accurate fast and fully automated spike sorting by developing and presenting our new method and spike sorting software.
We develop and make freely available a spike sorting toolbox called FSPS (Fuzzy SPike Sorting) which is designed to overcome mentioned limitations and allows a comparison of cross-laboratory clustering. The program allows continuous and triggered acquisition and offers the user the choice to set all parameters of the analysis automatically in case of online or unsupervised analysis, or to fine tune the parameters with an advanced interactive user interface for off-line analysis.