Computational Neuroscience

The Computational Neuroscience program aims to extract the computational principles of real neural circuits and systems to create novel, more powerful algorithms for pattern recognition and control. This research also seeks to develop large-scale realistic cortical and forebrain models capable of executing machine perception, learning, motor control and cognitive skills. The ultimate objective is to develop brain-based intelligent systems that can be embedded into autonomous platforms and robots and to understand the neural basis of cognitive skills.

Research Concentration Areas

  • Neural network dynamics and neural algorithms
  • Computational models of memory processes
  • Sensorimotor control and spatial navigation

Research Challenges and Opportunities

  • Interdisciplinary basic research to identify the principles of real neural circuits and systems and to produce computational models and novel neural algorithms
  • Basic research that seeks to characterize the neural dynamics of neural circuits, in particular cerebral cortex and large forebrain networks, involved in information routing, perception, attention, decision making, memory and action planning
  • Basic and applied research that seeks to implement neural systems in compact neuromorphic hardware and hybrid neural-AI systems
  • Interdisciplinary basic research aimed at elucidating how synaptic, cell, circuit and neural systems-level interactions enable memory mechanisms through computational modeling informed by neuroscience experiments
  • Basic research aimed at identifying and modeling the neural mechanisms of working memory, memory consolidation and memory retrieval with the aim of developing intelligent systems with human-like associative memory skills
  • Basic research that combines experiment and modeling that explores the computational consequences of dendritic spine clusters and dendritic processing in learning and sensory representations and could lead to possible neuromorphic VLSI designs with dendritic elements
  • Basic research aimed at identifying new control and learning principles from the neuroscience of motor control, in particular higher motor control. The includes exploring new neuroscience motor control principles applied to robotic control of manipulation and sequential motor tasks
  • Basic research in spatial navigation and human spatial awareness

Program Contact Information

Name: Dr. Thomas McKenna

Title: Program Officer

Department: Code 341

Email for Questions:

Program Funding

Submit white papers, QUAD charts and full proposals for contracts to this email address: ONR Code 34 Research Submissions

Follow instructions within BAA for submission of grant proposals to website.

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