DishBrain biological neural network architecture (IMAGE)
Caption
(A) Schematic illustration of the DishBrain feedback loop, the simulated game environment, and electrode configurations. (B) A schematic illustration of the overall network construction framework. The spiking time series data are first transformed into a 3D space using t-SNE embedding. These lower-dimensional representations are then combined into a tensor, which is decomposed using Tucker decomposition. The K-medoids algorithm is then applied to identify consistent representative channels across all cultures. These channels become network nodes, and pairwise Pearson correlation values serve as edge weights. The network layout reflects the physical placement of channels on the MEA, with node colors distinguishing sensory (green) from motor (blue) regions. (C) Schematic comparing the information input routes in the DishBrain system (left) and the 3 implementations of the deep RL algorithms (right). In each design, the input information to the computing module (deep RL algorithms or DishBrain) is denoted by a vector I. Note that in the DishBrain system, while this figure depicts stylized waveforms for illustrative purposes, the actual stimulation consisted of discrete electrical pulses.
Credit
Moein Khajehnejad, Cortical Labs.
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