Advanced assemblies of hardware, software, and synthetic biology (wetware) have resulted in new methods to embody neural systems into simulated environments to elicit dynamic goal-directed activity (Kagan et al., 2023, Biotechnology Advances.). Evidence of learning from in vitro neurons was established in a simplified game of Pong through closed-loop real-time electrophysiological recordings and patterned stimulations via a system called DishBrain (Kagan et al., 2022. Neuron.). Analysis of dynamic activity changes within these networks revealed key correlates associated with the appearance of learning within these biological neural networks. Of note was that robust differences in neural criticality within cultures arose when cells were embodied in a structured information landscape compared to engaged in just spontaneous activity (Habibollahi et al., 2023, Nature Comms.). While these results were replicated across cortical cells obtained from both induced human pluripotent stem cells and primary mouse preparations, significant limitations in the system still existed. To address these limitations a new system has been developed from the ground up. Firstly, a perfusion circuit capable of real-time adjustments of temperature, CO2, O2, and pressure level was developed to maintain a consistent homeostatic environment for the cells. The perfusion circuit was then integrated with custom hardware capable of allowing complex electrophysiological interactions with neural cells with minimal latency (<2ms). Software was produced to allow closed-loop real-time environments to be easily developed and facilitate rapid iteration of parameters. This platform has facilitated the electrophysiological assessment and manipulation of neural tissue with minimal noise in recordings using a variety of setups.