The field of artificial intelligence is undergoing a significant shift towards biologically inspired models, with a focus on incorporating temporal dynamics, neural synchronization, and higher mental states into deep learning architectures. This direction is driven by the need to create more powerful and generalizable AI systems that can learn and adapt in complex environments. Recent innovations have led to the development of models that can perform tasks requiring complex sequential reasoning, such as image classification, question-answering, and reinforcement learning. Notably, the integration of multimodal sensing, perception-cognition-action functions, and neuroplasticity-based memory storage has enabled the creation of more human-like AI systems. Furthermore, research has highlighted the importance of relative position predictivity in object recognition and the use of probabilistic schema induction for learning visual compositional concepts. Overall, the field is moving towards a more nuanced understanding of intelligence and cognition, with a focus on developing AI systems that can learn and adapt in a more human-like way. Noteworthy papers include: The Continuous Thought Machine, which introduces a novel deep learning architecture that incorporates neuron-level temporal processing and neural synchronization. Beyond Attention, which proposes a model that emulates high-level perceptual processing and awake thought states to pre-select relevant information before applying attention. Neural Brain, which outlines a unified framework for the development of embodied agents with human-like adaptability. Few-Shot Learning of Visual Compositional Concepts, which introduces a prototype model that employs deep learning to perform analogical mapping over structured representations of examples.