2026 Volume 38 Issue 2 Pages 660-664
History-dependent responses (HDRs) are phenomena in which prior stimulus history influences subsequent neuronal responses; they are considered fundamental to higher-order information processing such as memory and decision-making. In this study, we propose a method utilizing cultured neuronal networks to extract history-dependent neuronal activity from response activities to two sequential stimuli. The neuronal activity was binned into 1-ms time windows, and the spatial distribution of simultaneously firing electrodes was defined as an instantaneous spatial pattern (ISP). This sequential ISP data was then converted into images (visualized) and input into a Deep Convolutional Neural Network (CNN). These converted images of neural response activity, obtained under various inter-stimulus interval (ISI) conditions, were trained and classified by a deep CNN. The estimated discrimination accuracy for each class (i.e., the stimulating electrode) was evaluated as an index for the emergence (manifestation) of history-dependent neural activity. As a result, it was confirmed that the discrimination accuracy decreased depending on the interval of the two sequential stimuli. Specifically, under long ISI conditions, the evoked response patterns became similar, leading to this reduced discrimination accuracy. It was shown that the two evoked response patterns were distinct, and discrimination accuracy was high, approximately 1–2 s after the evoked stimulation, indicating that the history (memory) persisted during this period. Furthermore, an analysis of the contribution ratio for discrimination suggested that under short ISI conditions, the firing times tended to align across sweeps, suggesting that the internal state of the neuronal network may be forming a temporary stable state.
