Theoretical Neuroscience

Theoretical neuroscience is the application of mathematics and computational models to the study of the brain. Theorists use techniques such as network modeling, graph theory, and symbolic logic to develop models of neural circuitry and behavior. Theoretical neuroscience draws from a range of disciplines including computer science, mathematics, statistics, and psychology. Its broad scope includes the study of cognitive processes, decision making, behavior, and emotion. In the past few decades, theoretical neuroscience has made significant advances in understanding the brain’s functions. Researchers have developed models which demonstrate how the activity of neurons in the brain produces higher level functions, like encoding memories or understanding language. These models are used to study how the brain recognizes and processes information, encodes memories, and controls behavior. These models are critical for understanding disorders such as Alzheimer’s or schizophrenia. Theoretical neuroscience also plays an important role in developing agents which can mimic or even replicate some of the brain’s functions. These agents, such as robots or autonomous vehicles, use theoretical neuroscience principles to move, learn, and make decisions based on their environment. The field of theoretical neuroscience is rapidly expanding and is being applied to a wide range of disciplines such as robotics, artificial intelligence, and cognitive science. By understanding how networks of neurons produce higher level functions, the goal of theoretical neuroscience is to build better robots and AI agents based on the principles of the brain. By using these principles to build intelligent agents, we can create agents which can learn, adapt, and make decisions in an unpredictable environment.