Computational neuroscience is the study of brain function in terms of the nervous system’s information processing capabilities, such as those exhibited by neurons as they interact in circuits, ensembles and systems via electrical and chemical signals. Computational neuroscience models allow for generating hypotheses about learning and memory, cognition and arousal among other brain functions.

A hallmark of how our brains work is the interactions of neurons in circuits via dynamically formed connections called synapses. Picower scientists identify, map, and analyze circuits involved in learning and memory, emotion and behavior, and other brain functions both in health and disease.

A requirement of learning and memory is a brain capable of stably encoding change. Throughout our lives, in response to our experiences, our neurons form new synaptic connections and prune away others. Scientists in the Picower Institute study these processes of plasticity, elucidating their workings down to the molecule, to better understand how they work.

A typical neuron has thousands of synapses that connect it with other neurons in neural circuits. The location, type and constantly changing strength of each of these synapses determine how each neuron plays its role in the brain and how circuits are remodeled by experience. Research at the Picower Institute to map synapses is therefore essential to understanding how neural connections underlie brain functions and disease.