Chimera States in Neuro-Inspired Area-Efficient Asynchronous Cellular Automata Networks

Tsakalos, Karolos-Alexandros; Dragkola, Paraskevi; Karamani, Rafailia-Eleni; Tsompanas, Michail‐Antisthenis; Provata, A.; Dimitrakis, P.; Adamatzky, Andrew

doi:10.1109/tcsi.2022.3187376

Cited by 12 publications

(5 citation statements)

References 61 publications

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“…3.1 Benchmark spiking neural network for comparison Before designing the ergodic SL spiking neural network, a benchmark spiking neural network [22] is introduced, which uses the following Izhikevich's neuron model [24,25].…”

Section: Large-scale Ergodic Sl Spiking Neural Network and Biological...mentioning

confidence: 99%

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Shiomi,

Torikai

2024

IEICE Electron. Express

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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-processorbased spiking neural network. It is then discussed that the presented network will be a useful building block in a brain prosthetic device.

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Section: Large-scale Ergodic Sl Spiking Neural Network and Biological...mentioning

confidence: 99%

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Shiomi,

Torikai

2024

IEICE Electron. Express

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“…limit cycles) have demonstrated that amplitude variations may also occur in coupled networks, especially in the case of chaotic oscillators. In particular, coupled Lorenz systems or other chaotic oscillators lead to chimera states with variable amplitude and frequency [12][13][14][15][16]. These studies have lead to the search for chimera states with pure amplitude variations and are now known under the terms: pure amplitude chimeras, amplitude-mediated chimeras, amplitude-death chimeras and amplitude-modulated chimeras [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Amplitude chimeras and bump states with and without frequency entanglement: a toy model

Provata

2024

J. Phys. Complex.

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When chaotic oscillators are coupled in complex networks a number of interesting synchronization phenomena emerge. Notable examples are the frequency and amplitude chimeras, chimera death states, solitary states as well as combinations of these. In a previous study [Journal of Physics: Complexity, 2020, 1(2), 025006], a toy model was introduced addressing possible mechanisms behind the formation of frequency chimera states. In the present study a variation of the toy model is proposed to address the formation of amplitude chimeras. The proposed oscillatory model is now equipped with an additional 3rd order equation modulating the amplitude of the network oscillators. This way, the single oscillators are constructed as bistable in amplitude and depending on the initial conditions their amplitude may result in one of the two stable ﬁxed points. Numerical simulations demonstrate that when these oscillators are nonlocally coupled in networks, they organize in domains with alternating amplitudes (related to the two ﬁxed points), naturally forming amplitude chimeras. A second extension of this model incorporates nonlinear terms merging amplitude together with frequency, and this extension allows for the spontaneous production of composite amplitude-and-frequency chimeras occurring simultaneously in the network. Moreover the extended model allows to understand the emergence of bump states via the continuous passage from chimera states, when both ﬁxed point amplitudes are positive, to bump states when one of the two ﬁxed points vanishes. The synchronization properties of the network are studied as a function of the system parameters for the case of amplitude chimeras, bump states and composite amplitude-and-frequency chimeras. The proposed mechanisms of creating domains with variable amplitudes and/or frequencies provide a generic scenario for understanding the formation of the complex synchronization phenomena observed in networks of coupled nonlinear and chaotic oscillators.

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“…To meet the increasing demand for real-time and large-scale neuromorphic processors, previous studies have proposed a reconfigurable neuromorphic model based on fieldprogrammable gate array (FPGA) technology and asynchronous cellular automata [16][17][18][19][20][21][22][23]. These models offer hardware-efficient solutions for various applications, including Parkinson's treatment emulation, central pattern generation for hexapod robots, spike-timingdependent synaptic plasticity, neural integrators, tumor immunotherapy, and ergodic cellular automaton neuron models [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Implementing these models in FPGAs offers lower power consumption and hardware requirements compared with conventional models. The asynchronous cellular automaton neuron (ACAN) model, initially introduced in [24] and further optimized in [22], reproduces neuromorphic behaviors of cortical neurons using discrete-state dynamics, and it requires fewer hardware resources. Its dynamic adjustability after implementation makes it a versatile and suitable solution for implementing SNNs, and it is ideal for real-time neuromorphic applications [25,26], including movement classification tasks.…”

Section: Introductionmentioning

confidence: 99%

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A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications

Passias,

Tsakalos,

Kansizoglou

et al. 2024

Biomimetics

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This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach’s efficacy in precise movement activity classification. This method’s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs’ superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

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Chimera States in Neuro-Inspired Area-Efficient Asynchronous Cellular Automata Networks

Cited by 12 publications

References 61 publications

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Amplitude chimeras and bump states with and without frequency entanglement: a toy model

A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications

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