Starting a synthetic biological intelligence lab from scratch.

Abstract

Recent advances in artificial intelligence (AI) have led to the development and deployment of gigantic models trained on billions of samples. While training these models consumes enormous energy, the human brain produces similar outputs with dramatically lower data and energy requirements. This has increased interest in synthetic biological intelligence (SBI), which involves training <i>in vitro</i> neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, biomaterials, signal processing, computer programming, neuroscience, and AI. As a result, starting SBI research is highly nontrivial and time-consuming, as most labs specialize in either the biological aspects or the computational ones. Here, we propose how a computational lab can become familiar with the biological aspects of SBI and also discuss computational aspects for biological labs that are interested in SBI. We describe general strategies as well as step-by-step processes, risks, and precautions to mitigate delays and minimize costs.