Theoretical models are a valuable partner to experimental biology. Models provide the means to interpret existing biological data, make testable predictions, and can guide the design of experiments. As Einstein famously said to Heisenberg, it is the theory that determines what we can observe.
For three decades, The Neurosciences Institute has developed a theoretical approach called synthetic neural modeling, where large-scale computer simulations of nervous systems based on realistic approximations of anatomical and physiological data are used to learn about the brain. The functions of the nervous system arise only as it interacts with the rest of the body and as an animal engages in a behavior in the world, so we engage our synthetic neural models with behavioral tasks and embody them in robot-like devices. These brain-based devices (BBDs) learn from their own experience in their environment, just like we do. They have been used to study the neural bases of perception, operant and fear conditioning, episodic and spatial memory, navigation, and motor control. This approach has yielded a number of important insights and led to predictions that have been confirmed in studies of living animals.