I am a cognitive scientist who develops and studies brain-inspired computational models of learning and cognition. My interests and experience lie in mathematical and neural network models of cognitive processes. For more information on my research interests, please see my research page.

You can also view my publications page or download my CV in PDF form here.

From a theoretical perspective, computational models are useful for understanding how principles of neural circuits can explain cognition and behavior. One way they do this is that they make it easy for investigators to make changes to the assumptions used in building the model, effectively allowing investigators to perform highly-controlled experiments.

From a practical perspective, implemented models can be used to make testable predictions about behavior and/or brain activity. If the model makes correct predictions in many circumstances, it is likely to be correct.  For example, one interesting application of computational models is to use their predictions to fit behavioral data and analyze fMRI data (i.e., model-based analysis).

My modeling philosophy has two components: (1) models should be simple enough for clarity and communicability; and, (2) knowledge from neuroscience provides additional constraints that make models more likely to be correct.   The latter point is important because radically different models could generate the same behavior in a given experiment.