Understanding how integrated neural architectures generate coherent behaviors remains a primary challenge in neuroscience. The Little Brain Project is a research proposal aimed at understanding brain function and neural circuits through the lens of evolutionary processes. Moving away from isolated circuit analysis, this nascent project leverages Braitenberg’s synthetic psychology and Brooks’ subsumption architecture to investigate how complex adaptive behaviors emerge across phylogenetic evolution from integrated sensorimotor loops. To support this effort, we introduce the Little Brain Project (LBP) Simulator, a newly developed platform integrating PyTorch-based neural networks with MuJoCo physics and real robots. The environment simulates multimodal interactions—such as visual retinas, active whisker sensing, and polarized light navigation—alongside neural features like ring attractors and neuromodulated internal states. Future milestones will introduce neuromodulator-driven Hebbian learning, using systematically evolved physical robots to investigate how integrated neural architectures generate coherent adaptive behaviors.