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Theoretical and Computational Neuroscience Program

Overview

This program supports basic experimental and theoretical research focusing on biophysically realistic computational approaches modeling dynamical processes in the brain, from single cell activity, to neural systems regulating complex behaviors. This program provides support for the overall areas of interest and priorities of the Division of Neuroscience.

We encourage rigorous data-driven approaches integrating neurophysiological, neuroanatomical, or neurochemical techniques to accurately and unbiasedly decipher the mechanisms underlying brain functions and behavior.

Areas of Emphasis

  • Biologically realistic and theory-driven computational models of neuronal and non-neuronal processes from single cells, to networks, to systems, and behavior
  • Machine learning algorithms combined with effective explanatory techniques
  • Multi-modal data fusion algorithms to link distinct levels of analysis to brain and behavioral measures
  • Models for adaptive close-loop neuromodulation of behavioral outcomes
  • Machine learning models that continually improve performance beyond the training phase (e.g., when applied to classify, predict, modify, and explain real-world neuro-behavioral states)
  • Models leveraging data from innovative neurotechnologies
  • Methods simultaneously performing dimensionality reduction and features extraction in large non-linear systems (e.g., phenotyping activity-patterns of cells, circuits, and functional nodes and edges)
  • Studies focusing on understanding how differences in neuronal cell types affect information processing and behavioral outcome
  • Models enhancing the unbiased classifications and predictions of high-resolution behavioral data with electrophysiological and/or neurochemical units of analysis

Areas of Lower Priority

  • Computational projects where the primary focus is not to classify, predict, explain, and/or modify brain and behavior activity
  • Basic mechanistic studies of motor systems and motor function
  • Computational projects that model single level of analysis
  • Projects that investigate candidate genes lacking genome-wide association
  • Models that are not experimentally testable and validated

Applicants are strongly encouraged to discuss their proposals with the Institute contact listed below prior to the submission of their application to ascertain that their proposed work is aligned with NIMH funding priorities.

Applications should adhere to published recommendations detailed in a notice in the NIH Guide (NOT-14-004) and summarized in Enhancing the Reliability of NIMH-Supported Research through Rigorous Study Design and Reporting on the NIMH website.

Contact

Michele Ferrante, Ph.D.
Program Chief
6001 Executive Boulevard, Room 7202, MSC 9637
301-443-1576, michele.ferrante@nih.gov