Neuronal electrical activity pattern extracted by 3D clustering and discriminated by a deep CNN

Analyzing the dynamics of neural activity patterns using an electrophysiological approach is important for understanding the basis of information processing in a brain. In this study, we attempted to extract units of neuronal activity representing certain information and to discriminate activity pattern evoked by electrical stimulus and spontaneous activity without stimulation. We applied X-means clustering to the triplet of “2D-spacial coordinates and timestamps” of neuronal electrical spike bursts and the extracted single spatiotemporal neuronal activity pattern was converted into a standardized $8\times 84$ 2D-spatial pattern map. Most 2D-spatial patterns emerged repeatedly during recording time, corresponding to a representative motif. Then we gathered similar 2D-spatial patterns (primary clusters) into one ”pattern repertoire” and attempted to classify these pattern repertoires depending on the stimulus inducing the pattern repertoire by VGG16 deep convolutional neural networks (deep CNN). For that, we prepared two types of $224\times 224$ images by converting the 2D-spatial pattern maps, the spatial information priority neural activity pattern image (SIP-NAP image) and the time information priority neural activity pattern image (TIP-NAP image). As a result, over 80% of high discrimination accuracy was obtained for both types of VGG16 input images, especially 99.7% for TIP-NAP image. It suggested that spatiotemporal features contributing to the discrimination between spontaneous and evoked response activities were extracted by 3-D-crustering. In addition, it is shown that the spatial pattern of neuroelectric activity is able to be discriminated with high accuracy by combining transfer learning and SIP-NAP / TIP-NAP image translation method, even with a small amount of training data.