FIGURE 12 - available via license: Creative Commons Attribution 4.0 International
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Oscilloscope snapshots showing typical time waveforms and corresponding phase plane trajectories of the ergodic cellular automaton gene-protein network model implemented by the FPGA. The parameter values are as in Fig. 6. (a) Oscillating orbit corresponding to the periodic orbital set Op in Fig. 6(a). α0 = 175. (b) Resting orbit corresponding to the stable equilibrium set E in Fig. 6(c). α0 = 185.
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![FIGURE 1: Schematic diagram of a gene-protein network model [18].](publication/366550088/figure/fig1/AS:11431281109241709@1671842688746/Schematic-diagram-of-a-gene-protein-network-model-18_Q320.jpg)
A novel ergodic cellular automaton model of gene-protein network is presented. It is shown that the presented model can predict occurrences of typical nonlinear phenomena of a conventional ordinary differential equation gene-protein network model. In addition, theoretical analysis methods of the presented model are proposed. Using the analysis meth...
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... Then, in Section II, we present a Hes1 gene-protein network model [11,12,13] based on the nonlinear dynamics of the delay ergodic cellular automaton, where the ergodic cellular automaton has been used to design hardware-efficient biomimetic models [14,15,16,17,18,19,20,21,22]. It is also shown that the presented model can reproduce the occurrence mechanism of a typical bifurcation phenomenon of the DDE model. ...
... This study has clear novelty and significance as follows. (i) The presented model is the first gene-protein network model with a time delay that is designed based on the nonlinear dynamics of the single-clock ergodic cellular automaton, while the previous model in [15] has no time delay. This is a significant improvement since most gene-protein networks have a time delay [23,24,25,26]. ...
This paper presents a hardware-efficient delay ergodic cellular automaton gene-protein network model. It is shown that the presented model can reproduce the occurrence mechanism of one of the most typical nonlinear phenomena (supercritical Hopf bifurcation) of a mathematical gene-protein network model. Then the model is implemented by a field programmable gate array and experiments validate its operations. Furthermore, it is shown that the presented model is much more hardware-efficient compared to DSP-based and CPU-based gene-protein network models. It is then discussed that the presented model will be useful as a building block of a large-scale genome simulator for personalized medicine and drug discovery.
A spiking neural network of ergodic sequential logic neuron models is presented. It is shown that the presented network is capable of reproducing various biologically plausible spatio-temporal phenomena (e.g., basic synchronization and complicated chimera phenomenon) observed in the brain. Moreover, it is revealed that the presented network is able to operate with lower power compared to a standard digital-processor-based spiking neural network. It is then discussed that the presented network will be a useful building block in a brain prosthetic device.
