This talk focuses on our recent research at the intersection of neuroscience and artificial intelligence, including the evaluation and enhancement of large language models (LLMs) and building gaming AI agents. I will present studies on the behavioral evaluation of various LLMs, the development of neuroscience-inspired architectures for LLMs, the investigation of latent representations and cognitive maps within these models, and the application of in-context learning to emulate human-like reasoning and decision-making. I will note challenges and solutions in auditing and improvement of LLM-based text evaluation and discuss broader implications of these advancements for creating human-like agents and generative AI. I will also discuss our practical research in gaming AI, focused on using diffusion models and behavioral methods for building human-like game agents. This synthesis of ideas from recent studies provides insights into how neuroscience-inspired approaches to AI can lead to more human-like artificial intelligence systems.