An attention enhanced deep learning framework using ConvNeXt V2 for robust multi-class classification of colonoscopic images that demonstrates the effectiveness of modern convolutional architectures with embedded attention mechanisms in improving diagnostic performance in the analysis of colonoscopic images.
Xiaosheng Jin, Lu-Xi Chen, Liwei Xue et al.· Frontiers in Oncology· 0 citations
Understanding the neural mechanisms underlying general anesthesia remains a significant challenge in neuroscience and clinical practice. Traditional software-based simulations of large-scale brain networks are often constrained by high computational costs and fail to achieve real-time performance.
In this paper, we propose a high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics. The system successfully models a cortical network comprising 10,000 spiking neurons (8,000 excitatory and 2,000 inhibitory) utilizing the biologically plausible Izhikevich neuron model. Deployed on a field-programmable gate array (FPGA), the proposed architecture exploits high parallelism to achieve real-time simulation speeds. By adjusting synaptic weights and network parameters to mimic the pharmacological eects of anesthetic agents, our system can continuously monitor and evaluate state transitions in neural synchronization and firing patterns.
The results demonstrate that the hardware-accelerated neuromorphic approach provides an efficient, scalable, and real-time platform for investigating large-scale neural dynamics.
Pending future validation against empirical clinical EEG data, this foundational framework paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
Chuan-Guang Wang, Xiaotian Pan, Si Chen et al.· Frontiers in Systems Neurosc...· 0 citations
