Different Neural Network

Research on different neural network architectures focuses on understanding their internal workings, optimizing their performance for specific tasks, and minimizing their energy consumption. Current investigations explore diverse architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), transformers (like BERT), and spiking neural networks (SNNs), analyzing their representational topologies and comparing their effectiveness across various applications such as financial forecasting, image classification, and brain-computer interfaces. These studies aim to improve model efficiency, transferability, and interpretability, ultimately leading to more powerful and resource-efficient AI systems with broader applicability.