Spike sorting consists in clustering groups of signals based on their shapes.
I have always seen convolutional neural networks (CNNs) being used for image classification. Nevertheless, in this case, the inputs that need to be classified would be one-dimensional. Would this be a problem for a CNN to achieve good results?
There are some things that I'm not sure about this being a good approach.
- The two-dimensional kernels would become one-dimensional FIR filters. For these to extract relevant features, shouldn't all the spikes in the signals be in the same position? Would the recognition be position (or time, as we are talking about signals) invariant?
- What about the pooling layers? Do they make sense if we are talking about 1D patterns?
- Would it be a better approach to make the inputs of the CNN be pictures of the signals? For instance, an image from an oscilloscope instead of an array containing the values the signal takes for each instant of time. I don't think this would be very robust, but it was just an idea that crossed my mind.