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Generalize parameter optimization routines for the Human NeoCortical Neurosolver

Active, Closed for joining

Contributor: Carolina Fernandez

Mentors:Nicholas Tolley, Stephanie Jones, Mainak Jas, R. Thorpe


The Human Neocortical Neurosolver (HNN) is open-source, computational neural modeling software that allows us to examine the cellular- and circuit-level basis of brain responses. HNN requires the hand-tuning of a large set of parameters until a close fit between simulated and recorded data is attained. This hand-tuning can take a substantial amount of effort thus it is in the user’s best interest to automate the process so that parameters can be optimized in a time efficient manner. The goal of this project is to develop optimization functions that will perform a wide search over the parameter space to arrive at faithful simulations. HNN is currently being used to develop or test hypotheses about underlying circuitry that gives rise to cognitive processes of interest. Developing a robust algorithm for parameter optimization has the potential to illuminate avenues for the diagnosis and treatment of multiple brain disorders and diseases, cognitive impairment, and psychiatric disorders. Finally, contribution to HNN’s codebase will aid researchers who use the tool in yielding important constraints to the development of theories about the origins of human brain responses.

Completed Deliverables
  • Development of optimization functions for automatic parameter-space searching when modelling simulation
  • Algorithm development for more efficient tuning of model parameters