Spiking neural networks provide accurate and time-efficient models for whisker stimulus classification of the awake mouse.

Machine learning algorithms have great potential for classifying brain activity, and lightweight classifier algorithms, requiring little computational resources, can be used on low-energy neuromorphic hardware designed for implantable neuroprosthetics. One of these efficient algorithms, the Liquid State Machine, implements the concept of Spiking Neural Networks and has been shown to achieve outstanding results on the task of whisker stimulus detection from the mouse barrel cortex, a widely used model system. While this is promising for neuroprosthetics, it has been unclear how a Spiking Neural Network or other machine learning algorithms perform on data recorded from awake mice and how trained models generalize across individuals, the latter being relevant to transferring trained models to new hardware. Using laminar multi-electrode local field potential recordings obtained from four mice performing a single-whisker detection task, we benchmarked the performance of a collection of lightweight classification algorithms. We found that the Liquid State Machine, a generalized linear model, and the time series classifier ROCKET are the most accurate for stimulus detection. Among those, the Liquid State Machine achieved the fastest model training and inference runtime and provided robust accuracy across individual mice. Additional analyses show that there is no significant improvement in using multiple cortical layers as input for the model and that 40 ms of stimulus recording is sufficient to maintain high detection accuracy.