Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicle in Intelligent Transportation Gateway

Yang, Shuangming; Tan, Jiangtong; Lei, Tao; Linares-Barranco, B.

doi:10.1109/tits.2022.3232231

Cited by 50 publications

(9 citation statements)

References 54 publications

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“…Yang et al ( 2022a ) also showed a spike-based framework combined with the entropy theory, called heterogeneous ensemble-based spike-driven few-shot online learning (HESFOL), that allows the neural network to learn fast on a few shots for diverse purposes. Another work from Yang et al ( 2022b ) used the entropy theory and recurrent spike neural networks to shape a spike-based framework with minimum error entropy, called MeMEE, and provided meta-learning capability for navigation, and another work, inspired by the functional neuroanatomy of the Basal Ganglia, (Yang et al, 2023b ), pointed at higher performances on smart traffic navigation, Internet of Vehicles, based on a neuromorphic approach in a scalable and fault-tolerant framework. Our work does not lie on spiking neuron models, as the mentioned works do, but highlights the essential dynamics supported by neural ring attractors, as a novel neural structure for driving social navigation for robots.…”

Section: Introductionmentioning

confidence: 99%

Ring attractor bio-inspired neural network for social robot navigation

Rivero-Ortega,

Mosquera-Maturana,

Pardo-Cabrera

et al. 2023

Front. Neurorobot.

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IntroductionWe introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments.MethodsThe navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents.ResultsThe results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model.DiscussionOur approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment.

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Section: Introductionmentioning

confidence: 99%

Ring attractor bio-inspired neural network for social robot navigation

Rivero-Ortega,

Mosquera-Maturana,

Pardo-Cabrera

et al. 2023

Front. Neurorobot.

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“…Several notable research works in the field are worth mentioning. For instance, the “Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicles in Intelligent Transportation Gateway (Yang et al, 2023 )” focuses on developing a fault-tolerant edge computing system for the Internet of Vehicle applications in intelligent transportation. “CerebelluMorphic” is a large-scale neuromorphic model and architecture designed for supervised motor learning (Yang et al, 2021c ).…”

Section: Resultsmentioning

confidence: 99%

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications

Sanaullah,

Koravuna,

Rückert

et al. 2023

Front. Comput. Neurosci.

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This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and low-performance loss. To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons. The findings of the study provide valuable insights into the benefits and challenges of SNNs and their models, emphasizing the significance of comparing multiple models to identify the most effective one. Moreover, the study quantifies the number of spiking operations required by each model to process the same inputs and produce equivalent outputs, enabling a thorough assessment of computational efficiency. The findings provide valuable insights into the benefits and limitations of SNNs and their models. The research underscores the significance of comparing different models to make informed decisions in practical applications. Additionally, the results reveal essential variations in biological plausibility and computational efficiency among the models, further emphasizing the importance of selecting the most suitable model for a given task. Overall, this study contributes to a deeper understanding of SNNs and offers practical guidelines for using their potential in real-world scenarios.

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“…These platforms are built biologically motivated, mimicking some aspects of the brain architectures, e.g., a cerebellum network for motor learning (Yang et al, 2021c), dendritic on-line learning (Yang et al, 2023b), and context-dependent learning (Yang et al, 2021a). Based on the platform for context-dependent learning, a real-world application for smart traffic systems was developed (Yang et al, 2023a). Moreover, recent work showed that structural plasticity can be modeled on some of these platforms (Bogdan et al, 2018, Billaudelle et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Kaster,

Czappa,

Butz-Ostendorf

et al. 2023

Preprint

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Memory formation is usually associated with Hebbian learning, using synaptic plasticity to change the synaptic strengths but omitting structural changes. Recent work suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this work is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve this by substituting the random synapse formation with the Model of Structural Plasticity. As a homeostatic model, neurons regulate their own activity by growing and pruning of synaptic elements based on their current activity. Utilizing synapse formation with respect to the Euclidean distance between the neurons with a scalable algorithm allows us to easily simulate 4 million neurons with 343 memory engrams. These engrams do not interfere with one another by default, yet we can change the simulation parameters to form long-reaching associations. Our model paves the way for simulations addressing further inquiries, ranging from memory chains and hierarchies to complex memory systems comprising areas with different learning mechanisms.Author summaryMemory is usually explained by the strengthening or weakening of synapses that fire closely after each other. This limits the forming of memory to already connected neurons. Recent work showed that memory engrams can be formed with structural plasticity on a homeostatic base. In this model, the synapses of neurons have a fixed weight, and the model forms new synapses to have sufficient synaptic input to maintain a steady fire rate. However, this model connects neurons uniformly at random, limiting the parallel formation of memories. We extend this work by using a more biologically accurate model that connects neurons in a distant-dependent manner enabling us the parallel formation of memories. The scalability of our model enables us to easily simulate 4 million neurons with 343 memories in parallel without any unwanted inference. Our model increases biological accuracy by modeling brain-like structures such as cortical columns and paving the way for large-scale experiments with memory.

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Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicle in Intelligent Transportation Gateway

Cited by 50 publications

References 54 publications

Ring attractor bio-inspired neural network for social robot navigation

Ring attractor bio-inspired neural network for social robot navigation

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

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