International Journal of Neural Systems

Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment. By applying a reinforcement learning algorithm to the motor learning of elbow joint flexion task, simulation results successfully explained how visual-proprioceptive mismatch limits motor learning outcomes in terms of motor control accuracy and task completion speed. The larger the perceived angular offset between the two sensory modalities, the lower the motor control accuracy. Also, the more similar the peak reward amplitude of the two sensory modalities, the lower the motor control accuracy. In addition, simulation results suggest that insufficient exploration rate limits task completion speed, and excessive exploration rate limits motor control accuracy. Such a speed-accuracy trade-off shows that a moderate exploration rate could serve as another important factor in motor learning.

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer’s disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3–5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector’s complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because the single-level feature map cannot balance deep semantic information and shallow detail information. We introduce a novel detector — the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector to address these challenges. The SNPMiSo detector is constructed based on SNP-like neurons. In SNPMiSo, we employ two kinds of Transformers to boost the important features across different-level feature maps separately. After enhancing the features, we use an incremental upsampling module to upsample and merge the two feature maps. This combined feature map is input into the NAF dilated residual module and the NAF dual-branch detection head. This process allows us to extract multi-scale features and carry out detection tasks. Our tests show promising results: On the COCO dataset, SNPMiSo attains an Average Precision (AP) of 38.7, an improvement of 1.0 AP over YOLOF. In addition, SNPMiSo demonstrates a quicker detection speed, outperforming some advanced multi-level and single-level object detectors.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term “dynamic rhythm information.” We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ($meanScZ:meanHC=0.715:0.777$). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ($meanScZ:meanHC=0.695:0.629$), and the degree of coupling was negatively correlated with the duration of disease ($r=-0.373$, $p=0.003$). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral–maxillofacial surgery. Currently, the mainly available methods are traditional digitalized design methods that suffer from substantial artificial operations, limited applicability and high reconstruction error rates. An automated, precise, and individualized method is imperative for maxillofacial surgeons. In this paper, we propose a Stage-wise Residual Attention Generative Adversarial Network (SRA-GAN) for mandibular defect reconstruction. Specifically, we design a stage-wise residual attention mechanism for generator to enhance the extraction capability of mandibular remote spatial information, making it adaptable to various defects. For the discriminator, we propose a multi-field perceptual network, consisting of two parallel discriminators with different perceptual fields, to reduce the cumulative reconstruction errors. Furthermore, we design a self-encoder perceptual loss function to ensure the correctness of mandibular anatomical structures. The experimental results on a novel custom-built mandibular defect dataset demonstrate that our method has a promising prospect in clinical application, achieving the best Dice Similarity Coefficient (DSC) of 94.238% and 95% Hausdorff Distance (HD95) of 4.787.