Neural Network Activation

Neural network activation, the output of a neuron's computation, is crucial for network performance and interpretability. Current research focuses on developing novel activation functions, such as learnable activations, to improve model accuracy and efficiency across diverse tasks like image processing and 3D reconstruction, as well as analyzing activation patterns to enhance model explainability and understand internal computations. These efforts aim to improve both the performance and interpretability of neural networks, leading to more robust and trustworthy AI systems with applications across various fields. Furthermore, research is exploring efficient methods for converting deep neural networks into energy-efficient spiking neural networks, reducing computational costs.