Computation uses the tools of mathematics and computers to develop theoretical models that test and expand our understanding of the workings of brain and behavioral processes. Unlike the related field of artificial intelligence, computation seeks not just to create intelligence out of machines, but to illuminate the processes that underlie sensation and perception, control of action, learning and memory, language, and other cognitive processes. 

These theoretical studies offer the prospect of connecting diverse research constructs and paradigms, and of providing a new understanding of the algorithms that drive our “mental machinery.” 

BCS scientists are focused on three key areas of computation:

  • The study of the data representations and algorithms that autonomous systems might build to perform tasks that are important for human survival (closely related to artificial intelligence).
  • The implementation and testing of circuits that are constrained by neuronal data but aim to accomplish the tasks above.  
  • The development of analysis and statistical tools for analyzing and visualizing neuroscience data. 

Understanding something as complex as the human mind requires computational models that accurately translate the system’s internal workings. Models help us build formal bridges between any two levels of analysis. For example: from gene expression programs to regulation of neuronal connections (synapses), or from neuronal circuit connections to patterns of neuronal activity. Other examples include from patterns of neuronal activity to behavioral report and mental states, and last, from mental states to cognitive function. 

As we work to build a complete picture of the neural mechanisms of the mind, it is necessary for us to link models of all levels. Models allow us to make predictions about behavior, to emulate key aspects of neural computations in other devices (brain inspired computing), and to consider the best ways to repair or augment key functions.