Embodied Biocomputing Sequential Circuits with Data Processing and Storage for Neurons-on-a-chip
<|reference_start|>Embodied Biocomputing Sequential Circuits with Data Processing and Storage for Neurons-on-a-chip: With conventional silicon-based computing approaching its physical and efficiency limits, biocomputing emerges as a promising alternative. This approach utilises biomaterials such as DNA and neurons as an interesting alternative to data processing and storage. This study explores the potential of neuronal biocomputing to rival silicon-based systems. We explore neuronal logic gates and sequential circuits that mimic conventional computer architectures. Through mathematical modelling, optimisation, and computer simulation, we demonstrate the operational capabilities of neuronal sequential circuits. These circuits include a neuronal NAND gate, SR Latch flip-flop, and D flip-flop memory units. Our approach involves manipulating neuron communication, synaptic conductance, spike buffers, neuron types, and specific neuronal network topology designs. The experiments demonstrate the practicality of encoding binary information using patterns of neuronal activity and overcoming synchronization difficulties with neuronal buffers and inhibition strategies. Our results confirm the effectiveness and scalability of neuronal logic circuits, showing that they maintain a stable metabolic burden even in complex data storage configurations. Our study not only demonstrates the concept of embodied biocomputing by manipulating neuronal properties for digital signal processing but also establishes the foundation for cutting-edge biocomputing technologies. Our designs open up possibilities for using neurons as energy-efficient computing solutions. These solutions have the potential to become an alternate to silicon-based systems by providing a carbon-neutral, biologically feasible alternative.<|reference_end|>
@article{basso2024embodied,
    title={Embodied Biocomputing Sequential Circuits with Data Processing and
  Storage for Neurons-on-a-chip},
    author={Giulio Basso and Reinhold Scherer and Michael Taynnan Barros},
    journal={arXiv preprint arXiv:2408.07628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07628},
    primaryClass={cs.ET q-bio.NC}
}