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ucr cognitive & neural computation lab

PI: Megan Peters
we use neuroimaging and computational modeling to study how the brain represents and uses uncertain information and uncertainty itself

CNNS was a great success!

thank you all for coming, and we'll see you next year!


how do brains process and represent information?

our brains continuously filter, quantify, categorize, and make "us" aware of a deluge of sensory information with remarkable accuracy and precision. we behave adaptively in our environments and we learn. in many cases, the neural computations underlying these abilities appear to be mathematically optimal (but maybe not always). how do brains do this? how is noisy, ambiguous information represented in neuronal activity and neural connections? how does a brain know whether it has interpreted incoming information correctly? how does it learn what to expect, and when to update those expectations? what can we learn from human and animal neural processing that will be beneficial to development of artificial systems?

these are the types of questions we try to answer in the lab. we use an interdisciplinary approach drawing insights and methodologies from bioengineering/neuroengineering, computational neuroscience, cognitive science, psychology, and even philosophy. check out the projects page for more info.

techniques we use:



fMRI, EEG, & ECoG (humans)

via collaborations:
electrophysiology (primates, rats)
calcium imaging (rodents)


computational modeling

generative models, monte carlo simulation, Bayesian decision theory, signal detection theory, neural networks, machine learning



transcranial direct current stimulation (tDCS)

transcranial magnetic stimulation (TMS)

want to join?

we are seeking undergraduate researchers, MS students, and PhD students.