| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2023: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2022: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2021: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2020: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2019: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Outline of Final Research Achievements |
We have established a method for identifying activity patterns by combining 3D spatiotemporal clustering and deep transfer learning. In this study, we applied 3D spatiotemporal clustering to spontaneous and stimulus-induced extracellular potential patterns measured from a rat hippocampal neuronal network cultured on a multi-point measurement dish equipped with planar microelectrodes on the bottom, and visualized the spatiotemporal patterns of neuronal electrical activity. Using the pre-trained model VGG16 for transfer learning, we successfully identified response patterns induced by two distinct stimulation electrodes and spontaneous activity patterns with over 90% accuracy. Furthermore, it was suggested that the neural activation pathway codes information, and there are common streams in induced responses and spontaneous activities.
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