Discrimination of stimulus-response patterns in cultured neuron networks using deep learning

At HAI 2023, a dialogue agent using a cultured biological neuronal network as a semi-artificial brain was presented: "A semi-living broken dialogue agent depending on the internal state of a living neuronal network." This system identifies neuronal activity patterns elicited by "pleasant" and "unpleasant" inputs. To improve identification accuracy, we propose a method utilizing instantaneous spatial patterns and transfer learning in Deep Learning techniques (Figure 1). Using a multi-electrode array from cultured neural networks, the method analyzes electrical spikes within a 1 ms window to define instantaneous spatial patterns (ISP) capturing signal propagation pathways. Leveraging advanced deep learning techniques, particularly transfer learning with the VGG16 CNN model, enabled the method to identify neuronal activity from different responses with over 90% accuracy. Additionally, the study introduced two imaging techniques: TIP-NAP and SIP-NAP, effectively distinguishing neuronal activities. The results underscore the importance of temporal continuity in neuronal activity for accurate information representation.