Advanced neural network architectures (CNN, RNN, LSTM, GAN) Integration of JAX, TensorFlow, and PyTorch modules Quantum Neural Network integration Reinforcement learning capabilities Brain-Computer Interface (BCI) integration Fairness constraints and bias mitigation Adversarial training for improved robustness Interpretability tools (SHAP) 2D and 3D convolution support Data augmentation techniques Cognitive architecture with consciousness simulation AlphaFold integration for protein structure prediction Neural protein modeling for neuroscience applications Drug discovery support through protein structure analysis Synthetic biology insights from protein folding predictions Compatibility with numpy < 2 and torch 1.11.0 Integration of AlphaFold for advanced protein structure prediction Enhanced capabilities for neural protein modeling and drug discovery Quantum Neural Network module for quantum computing integration Improved Brain-Computer Interface (BCI) functionality Advanced cognitive architecture with consciousness simulation Support for multiple Python versions (3.9, 3.10, 3.11, 3.12)