Figure 2

Astrocyte-based logic gate design platform using reinforced learning. The platform is a feedback system for reinforced learning using state value functions that fine tunes the \(\hbox {Ca}^{2+}\) activated level as well as time slot for input signals \(T_b\). The Kernel of the platform considers information such as the delay of signals between cells in the population, noise, transmission period, required logic operation, input signal flow concentration of \(\hbox {Ca}^{2+}\) ions, which are fed into the data collection that is used for training. In the output of the platform, synthetic gene transcription is identified for the logic operation based on the defined values of the cellular signalling threshold the is tuned by the kernel state value functions. To validate the platform, a \(\hbox {Ca}^{2+}\) signalling-based molecular communications simulator is integrated. The simulator includes models for individual cell’s intra as well as intercellular signalling for a defined topology. The output population will produce \(\hbox {Ca}^{2+}\) signals based on the logic computation. In blue are the controlling blocks that define the threshold for the logic gate operation. The blocks within the ”Input Processing” are stages of the \(\hbox {Ca}^{2+}\) signalling process in each astrocyte cell.