Clustering neural spike trains with transient responses

The detection of transient responses, i.e. nonstationarities, that arise in a varying and small fraction of the total number of neural spike trains recorded from chronically implanted multielectrode grids becomes increasingly difficult as the number of electrodes grows. This paper presents a novel application of an unsupervised neural network for clustering neural spike trains with transient responses. This network is constructed by incorporating projective clustering into an adaptive resonance type neural network (ART) architecture resulting in a PART neural network. Since comparisons are made between inputs and learned patterns using only a subset of the total number of available dimensions, PART neural networks are ideally suited to the detection of transients. We show that PART neural networks are an effective tool for clustering neural spike trains that is easily implemented, computationally inexpensive, and well suited for detecting neural responses to dynamic environmental stimuli.