Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.

Research on emotion recognition in conversations emphasises the importance of complex relationships between conversational context and multimodality. Graph-based methods, particularly hypergraph-based methods have shown promise in capturing these relationships. However, challenges persist in avoiding redundant context while capturing essential information for optimal context embeddings and fully leveraging cross-modal complementarities for sufficient fusion. In contrast, the human brain flexibly retrieves relevant memories and integrates multi-modal data for accurate recognition. Based on this superiority, we propose BrainyHGNN, a brain-inspired hypergraph neural network. It integrates a Dynamic Memory Selector for contextual hyperedges, mimicking selective memory retrieval mechanisms for adaptive and modality-specific context retrieval. HierSensNet is designed for multi-modal hyperedges, mirroring hierarchical cross-modal interaction mechanisms to ensure effective multimodal fusion. Experimental results on two benchmark datasets validate the superior performance of BrainyHGNN, confirming the effectiveness of its innovative approach. This work highlights the potential of brain-inspired methods to advance flexible context retrieval and sufficient multimodal fusion, presenting a promising direction for future research in this domain.

Motion planning, considered a fuzzy sequential decision-making problem, encounters significant challenges due to inherent environmental uncertainty. Traditional planning methods that rely on single strategies often struggle in complex scenarios. While fuzzy systems excel at handling uncertainty, high-dimensional continuous spaces require a large number of fuzzy rules, which significantly increases computational complexity. In contrast, humans leverage limited and fuzzy information to address various decision-making scenarios flexibly and efficiently. The concurrent reasoning mechanism in the prefrontal cortex plays a crucial role during this process. Consequently, the brain-inspired model and the concept of multiple fuzzy rules offer a novel perspective for the above issues. Motivated by these insights, this article proposes a brain-inspired motion planning method called harmonized learning with concurrent arbitration (HLCA). Specifically, inspired by the concurrent inference model, a concurrent arbitration module is employed in the planning process to effectively manage the boundary between exploration and exploitation. Furthermore, inspired by the multistrategy processing mechanism, HLCA introduces multistrategy harmonized learning by referring to the mechanism for operating multiple fuzzy rules, allowing the dynamic selection of strategies through a reliability function to enable self-improving learning. Experimental results demonstrate that HLCA outperforms state-of-the-art benchmarks, highlighting its potential to enhance the planning performance of robots by learning from the human brain.

Efficient and reliable motion planning is essential for robots in transportation systems. Learning-based motion planners, especially those leveraging graph neural networks (GNNs), have emerged as promising approaches to accelerate motion planning. However, current GNN-based planners suffer from inevitable performance bottlenecks due to their insufficient exploitation of environmental information and the intrinsic connections within graph structures. In contrast, the human brain exhibits innate strengths in decision-making and reasoning, enabling complex inferences from sparse observations and rapid integration of new information to control behavior. Neuroscience evidence reveals the human brain translates decision-making problems into graph structures and sensory observations, leveraging spatial relational memory for sensory inference. To address these issues, this paper proposes a brain-inspired GNN-based motion planner, BrainyMP, which innovatively draws on the brain’s spatial relational memory mechanisms. Specifically, a selective sampling strategy is proposed to reduce unnecessary exploration during the construction of the random geometric graph (RGG). Additionally, a Brainy Edge Selector is designed to filter inappropriate edges, enhancing planning quality. Furthermore, the Memory-aware Predictor is proposed to improve graph pattern learning capabilities and planning efficiency by integrating subgraph structures. Extensive experimental results demonstrate that the proposed method significantly improves planning quality and efficiency in maze and robotic-arm manipulation tasks while maintaining high success rates. These results indicate that emulating human brain mechanisms holds promise for improving robotic performance.

Cross-sensory interaction is a key aspect for multisensory recognition. Without cross-sensory interaction, artificial neural networks show inferior performance in multisensory recognition. On the contrary, the human brain has an inherently remarkable ability in multisensory recognition, which stems from the diverse neurons that exhibit distinct responses to sensory inputs, especially the multisensory neurons with multisensory responses hence enabling cross-sensory interaction. Based on this neuronal diversity, we propose a Neuronal Diversity inspired Multisensory Recognition Model (ND-MRM), which, similar to the brain, comprises unisensory neurons and multisensory neurons. To reflect the different responses characteristics of diverse neurons in the brain, special connection constraints are innovatively designed to regulate the features transmission in the ND-MRM. Leveraging this novel concept of neuronal diversity, our model is biologically plausible, enabling more effective recognition of multisensory information. To validate the performance of the proposed ND-MRM, we employ a multisensory emotion recognition task as a case study. The results demonstrate that our model surpasses state-of-the-art brain-inspired baselines on two datasets, proving the potential of brain-inspired methods for advancing multisensory interaction and recognition.

Abstract The paper addresses the fixed-/preassigned-time synchronization of stochastic memristive neural networks (MNNs) with uncertain parameters and mixed delays. Adaptive sliding mode control (ASMC) technology is mainly utilized. First, a proper sliding surface is constructed and the adaptive laws are given. Also, the synchronization control scheme is designed, which can ensure error system to realize fixed-time stability. Second, preassigned-time sliding mode control scheme is mainly provided to realize fast synchronization of MNNs. The presented theoretical methods can guarantee the error system convergence and stability for reaching and sliding mode within preassigned-time. And the synchronization criteria and explicit expression of settling time (ST) are acquired, where ST is not related with initial values and controller parameters but can be predefined perferentially. Finally, the calculation example is offered to interpret the practicability and availability of the innovations in this paper.

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.

A common drawback of EEG-based emotion recognition is that volume conduction effects of the human head introduce interchannel dependence and result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide extra useful information, and they actually reduce the performance of emotion recognition. However, the existing feature selection methods, commonly used to remove redundant EEG features for emotion recognition, ignore the correlation between the EEG features or utilize a greedy strategy to evaluate the interdependence, which leads to the algorithms retaining the correlated and redundant features with similar feature scores in the EEG feature subset. To solve this problem, we propose a novel EEG feature selection method for emotion recognition, termed global redundancy minimization in orthogonal regression (GRMOR). GRMOR can effectively evaluate the dependence among all EEG features from a global view and then select a discriminative and nonredundant EEG feature subset for emotion recognition. To verify the performance of GRMOR, we utilized three EEG emotional data sets (DEAP, SEED, and HDED) with different numbers of channels (32, 62, and 128). The experimental results demonstrate that GRMOR is a promising tool for redundant feature removal and informative feature selection from highly correlated EEG features.

Bottom-up human pose estimation has raised more investigation in recent years, especially 2D keypoints regression. However, the state-of-art DEKR [1] still has some aspects (e.g., speed and accuracy) to be improved. In this paper, we propose a new framework named DEKRv2, which has been enhanced compared to DEKR. When DEKR calculates the offset of each keypoint, it only considers the features of the current keypoint and neglects the constraints between the adjacent keypoints. We adopt a coarse-to-fine feature extraction method to obtain a more accurate feature location of keypoints for this problem. We also find that the multibranch network in DEKR is very time-consuming because it is serial. We designed a more effective module based on Group Convolution to replace the multi-branches network in DEKR, and it can reduce reasoning time. Experiments on the CrowdPose dataset show that our method achieves superior compared with DEKR in speed or accuracy, respectively. In the single-scale test, our method obtains 66.6 mAP, 0.6 higher than DEKR. The codes and models are available at https://github.com/chaowentao/DEKRv2.

In this paper an object detection algorithm is proposed, which is robust in the presence of cast shadows and is based on geometric projections. The novelty of the work lies in the use of homology mapping of the foreground regions between different parallel planes within a monocular view, unlike some existing algorithms which depend on the use of multiple cameras. The results on an open video dataset are provided.

An object detection algorithm which can remove moving cast shadows is presented. It is based on the homography mapping of foreground regions from multiple cameras. Not only the homography for the ground plane but also those for multiple parallel planes are employed. Unlike the existing geometric approaches, this algorithm removes cast shadows while keeping the feet of moving objects.