Boosting learning ability of overdamped bistable stochastic resonance system based physical reservoir computing model by time-delayed feedback

Shi, Zhuozheng; Liao, Zhiqiang; Tabata, Hitoshi

doi:10.1016/j.chaos.2022.112314

Cited by 12 publications

(5 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially stochastic resonance (SR) 1 , as the most representative example of this fact, is a phenomenon that the response of a nonlinear system to a weak input periodic signal can be amplified by the assistance of optimal noise level. SR has been observed in various types of nonlinear systems, such as physical, chemical, mechanical, biological systems 2 – 5 . In the context of neural systems, SR has been documented in the caudal photoreceptors of crayfish and cricket 6 , 7 , predatory behavior of paddlefish 8 and mammalian brain 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Detecting stochastic multiresonance in neural networks via statistical complexity measure

Wu,

Sun

2024

Sci Rep

View full text Add to dashboard Cite

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay $$\tau$$ τ is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when $$\tau = nT_{e}$$ τ = n T e with $$n$$ n being a positive integer and $$T_{e}$$ T e being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at $$\tau = nT_{e} - 2$$ τ = n T e - 2 , as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

show abstract

Section: Introductionmentioning

confidence: 99%

Detecting stochastic multiresonance in neural networks via statistical complexity measure

Wu,

Sun

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For a reservoir computing system, the memory span can be quantitatively evaluated by two different methods. The first is benchmarking the system on short-term memory task with quantitatively defined short-term memory capacity as an index 41 . The second method is using information processing capacity to comprehensively evaluate the nonlinear and linear capacities of the binary input-driven reservoir computing system 42 , 43 .…”

Section: Introductionmentioning

confidence: 99%

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing

Liao

Yamahara

Terao

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu3Fe5O12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance.

show abstract

“…Due to its potential applications, significant efforts have been made in the past few decades to study the SR both theoretically [2,3] and experimentally [4][5][6]. These achievements have been extensively utilized in various fields, including physics, chemistry, biomedicine, and engineering [7][8][9][10]. Especially, SR was initially used to investigate climate change during the past 700000 years, as applied by Benzi et al [11] and Nicolis [12].…”

Section: Introductionmentioning

confidence: 99%

Detecting the subthreshold signal in a neural network via statistical complexity measure

Wu¹,

Sun²,

Liu³

2023

Phys. Scr.

View full text Add to dashboard Cite

This paper proposes an information theory approach for detecting the subthreshold signal in a small-world network composed of Fitz Hugh-Nagumo (FHN) neurons. Statistical complexity measure (SCM) and normalized Shannon-entropy (NSE) have been defined based on the specific and nonconsecutive firing time intervals series, and employed to quantify the stochastic multiresonance (SMR) phenomena in this small-world neural network. The results show that there are several maxima of SCM and several minima of NSE at various optimal noise levels, which is regarded as the signature of the occurrence of SMR. This also implies that the subthreshold signal can be accurately detected across multiple levels of noise. More intriguingly, we have discovered that the degree of this neural network can induce the generation of multiple resonance-like behaviors. In addition, our findings demonstrate that the SCM outperforms the traditional signal-to-noise ratio (SNR) in accurately identifying firing dynamical regularity under certain noisy conditions. Furthermore, the SCM can capture subtle resonance-like behavior induced by the degree, which the SNR is unable to detect. Thus, the SCM could have potential power on detecting weak signals in neural networks.

show abstract

Boosting learning ability of overdamped bistable stochastic resonance system based physical reservoir computing model by time-delayed feedback

Cited by 12 publications

References 64 publications

Detecting stochastic multiresonance in neural networks via statistical complexity measure

Detecting stochastic multiresonance in neural networks via statistical complexity measure

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing

Detecting the subthreshold signal in a neural network via statistical complexity measure

Contact Info

Product

Resources

About