Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence

Hu, Bin; Guan, Zhi‐Hong; Chen, Guanrong; Chen, C. L. Philip

doi:10.1109/tcyb.2021.3071110

Cited by 14 publications

(4 citation statements)

References 106 publications

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“…This outdated perspective is insufficient for predicting the extent of surface and air pollution caused by exhalations, necessitating a significant change in how this phenomenon is viewed [124]. By transitioning from the concept of isolated droplet emissions to the turbulent multiphase puff cloud model, we can gain a new perspective on how pathogens spread in indoor environments over time and space [125]. This shift can help assess the effectiveness of interventions related to indoor space management and occupancy by focusing on the air and surface contamination levels [126].…”

Section: Stochastic Integrationsmentioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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The spread of infectious diseases such as COVID-19 depends on complex fluid dynamics interactions between pathogens and fluid phases, including individual droplets and multiphase clouds. Understanding these interactions is crucial for predicting and controlling disease spread. This applies to human and animal exhalations, such as coughs and sneezes, as well as bursting bubbles that create micron-sized droplets in various indoor and outdoor environments. By exploring case studies in this regard, this study examines the emerging field of fluid dynamics in disease transmission, focusing on multiphase flows, interfacial flows, turbulence, pathogens, human traffic, aerosol transmission, ventilation, and breathing microenvironments. These results indicate that increased ventilation rates and local ventilation methods can effectively reduce the concentration of SARS-CoV-2-laden aerosols in the immediate breathing spaces between individuals. In a displacement-ventilated room, both neutral and unstable conditions were more effective in removing breathed SARS-CoV-2-laden aerosols from the air, regardless of the presence of test subjects. However, stable conditions may increase the risk of infection in individuals living in confined spaces. Thus, the findings of this study are useful for providing practical guidance for managing the spread of airborne infections. HIGHLIGHTS Fluid dynamics affect the transmission of infectious diseases such as COVID-19 This study explored multiphase fluid flow, aerosol dispersion, and respiratory zones Increased ventilation and local methods reduce SARS-CoV-2 aerosol concentration Displacement ventilation eliminates SARS-CoV-2 aerosols under unstable conditions Cramped and damp living environments can increase the risk of transmission of infection GRAPHICAL ABSTRACT

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Section: Stochastic Integrationsmentioning

confidence: 99%

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Koley

2024

Trends Sci

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“…Beyond their fundamental physical attributes, there has been a renewed interest in harnessing the nonlinear dynamic systems for information processing in the context of the burgeoning advancements in artificial intelligence and brain-inspired computation [9][10][11][12][13][14], where elegant and sophisticated spatio-temporal dynamics take in charge of processing analog signals for cognitive learning and continuous adaptation with high efficiency and low energy consumption [15][16][17][18][19][20]. In this context, dynamic molecular devices, exhibiting temporal switching and memristive characteristics, emerge as promising physical systems to emulate the behaviour of synapse and to realize the in-materia neuromorphic computing [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Exploring non-steady-state charge transport dynamics in information processing: insights from reservoir computing

Li,

2024

Neuromorph. Comput. Eng.

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Exploring nonlinear chemical dynamic systems for information processing has emerged as a frontier in chemical and computational research, seeking to replicate the brain's neuromorphic and dynamic functionalities. In this study, we have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical simulation by integrating a non-steady-state proton-coupled charge transport system into reservoir computing (RC) architecture. Our system demonstrated remarkable success in tasks such as waveform recognition, voice identification and chaos system prediction. More importantly, through a quantitative study, we revealed that the alignment between the signal processing frequency of the RC and the characteristic time of the dynamics of the nonlinear system plays a crucial role in this physical reservoir’s performance, directly influencing the efficiency in the task execution, the reservoir states and the memory capacity. The processing frequency range was further modulated by the characteristic time of the dynamic system, resulting in an implementation akin to a 'chemically-tuned band-pass filter' for selective frequency processing. Our study thus elucidates the fundamental requirements and dynamic underpinnings of the non-steady-state charge transport dynamic system for RC, laying a foundational groundwork for the application of dynamical molecular scale devices for in-materia neuromorphic computing.

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“…Bin Hu etc. suggested that it is a feasible way to reconstruct cortical networks with dynamic activities instead of using only artificial computing networks [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Recall Network: A Simple Brain-Inspired Algorithm for Classification

Tian

Liu

et al. 2022

Computational Intelligence and Neuroscience

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The latest development of neuroscience has deepened the understanding of the information-processing mechanisms in the human brain and inspired a couple of sophisticated computational methods, such as deep learning, memory networks, and hierarchical temporal memory. However, it remains a challenge to explore simpler models due to the high computational cost of the above-mentioned methods. This paper proposes recall network (RN), an intuitive and simple model, that initializes itself by constructing the network path derived from the correlation of features in the training dataset and then makes classification decisions by recalling the paths that are relevant to the features in the test set. The algorithm has been applied to 263 datasets available from UCI Machine Learning Repository, and the classification results of repeated 10-fold cross-validation experiments on Weka demonstrate its competitive performance with prestigious classification algorithms, such as ANN, J48, and KNN.

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Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence

Cited by 14 publications

References 106 publications

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Role of Fluid Dynamics in Infectious Disease Transmission: Insights from COVID-19 and Other Pathogens

Exploring non-steady-state charge transport dynamics in information processing: insights from reservoir computing

Recall Network: A Simple Brain-Inspired Algorithm for Classification

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