Recognition of instantaneous spatial patterns of evoked neuronal activity in cultured neural networks using deep learning.

Abstract

Deep learning highly effective in pattern recognition across various fields, but obtaining sufficient training data in the life sciences fields can be challenging due to the nature of biological data. In this study, we classified electrical activity pattern evoked by 2 different stimuli to a cultured neuronal network, by converting the time-ordered sequence of instantaneous spatial patterns into an image. These patterns were used as training and validation data for transfer learning with pretrained model, VGG-16. Instantaneous spatial patterns were generated by dividing the continuous evoked responses into 1 ms time windows, with the pixel brightness representing the firing rates for 10 evoked sweeps. Two types of images were provided to VGG- 16: “ Spatial Information Priority Neural Activity Pattern ”(SIP-NAP) images, which preserve electrode arrangement within the same time window and rearranged multiple images, “ Time Information Priority Neural Activity Pattern ” (TIP-NAP) images, which rearrange multiple images to ensure that adjacent brightness values (firing rates) of the same electrode are in different time windows. Classification accuracy evaluated for each type of image, with successful classification achieved at over 90% accuracy and rapid convergence in TIP-NAP. These results suggest the important role of temporal information flow, or the “ stream ”, plays an important role in representing information in a neuronal network.