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Center for Computational Neuroscience and Artificial Intelligence
Institute of Neuroscience
University of Oregon
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The NeuroAI Center serves as a nexus for research on computational aspects of neuroscience and applications of Artificial Intelligence to neural circuits and cognition. The Center is an interdisciplinary home for scientists on campus investigating theoretical approaches to brain function, including undergraduate and graduate students, postdocs, and faculty. It is a central node of the greater Pacific Northwest neuroscience community and a founding member of the International Network on Biologically-Inspired Computing.

A primary mission of the NeuroAI Center is training the next generation of neuroscientists to crack the neural code through our Interdepartmental Computational Neuroscience Program, a vibrant Seminar Series and our Workshop series in which local or visiting researchers lead hands-on tutorials applying cutting-edge methods from machine learning to neuroscience research. 

The Center enthusiastically promotes collaborations between theoretical and experimental labs on campus and beyond, including a diverse range of labs from computational, systems, cognitive and developmental neuroscience, to neuroengineering. Participating faculty hold positions in various departments including Biology, Mathematics, Physics, Psychology, and the Knight Campus for Accelerating Technological Impact. Our interdisciplinary and collaborative environment allows trainees the opportunity to explore different paths and develop training plans tailored to their career goals.

NeuroAI Calendar of Events

 
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 Research Projects

Three figures, labeled ABC, illustrating multiple input pathways for sound (A), a mathematical model of sound representation and learning in the striatum (B), and a drawing of a mouse listening to acoustic stimuli (C).
BRAIN Initiative Collaboration on "Distinct contributions of converging neural pathways to auditory learning"

To investigate how the nervous system flexibly links sensory stimuli to actions, this research focuses on auditory decision making in the multiple input pathways converging on the striatum, a key structure in the regulation of motor behavior. The central hypothesis is that the various auditory cortical and thalamic pathways to the striatum play distinct computational roles during learning and execution of sound-driven decisions. To investigate this, we are developing mathematical and neural-network-based models of sound representation and learning in these brain areas. In parallel, we will test and refine these models by applying them to experiments in mice trained to perform acoustic discrimination tasks.

Participating labs: Murray, Jaramillo

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BRAIN Initiative Collaboration on “Neural mechanisms of flexible behavior”
This project investigates the local and large-scale neural circuit mechanisms that underlie the brain’s ability to flexibly shift sensory processing depending on behavioral state and task demands. This flexibility is essential to normal cognitive function, and is disrupted in a range of psychiatric and neurological conditions.
Participating labs: McCormick, Niell, Jaramillo, Mazzucato, Wehr, Smear
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NSF CRCNS Collaboration “A theory of serotonergic neuromodulation”
Sensory perception originates from the integration of external stimuli with internal representations of the world, including prior expectations and behavioral states. In normal conditions, balancing new sensory evidence with internal models leads to flexible and accurate perception. Disrupted balance can lead to altered perception. The serotonin-2A receptor is associated with such perceptual alterations, both in its role in schizophrenia and in the action of psychedelic drugs. We leverage a synergistic collaboration between theory and experiment to establish a new theoretical framework for dissecting the neural mechanisms of serotonergic modulation, based on a biologically plausible model of cortical processing, informed by the observed neuromodulation effects. We combine modeling and experiment to explain the circuit mechanism mediating the effect of serotonergic modulation.
Participating labs: Mazzucato, Niell
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NIMH funded collaboration “Targeted cortical manipulations to augment cognitive functions”
Overview: A long-standing goal of neuroscience is to understand how cognitive functions are implemented through interactions of large neural ensembles in order to provide practical solutions for altering cognitive behavior through manipulations of neural circuits. We develop a theoretical framework to infer functional causal interactions through sparse sampling of large neural ensembles and test our framework on the prefrontal cortex of the monkey brain. This advance enables us to innovate a technique for targeted manipulation of the prefrontal circuit and alter the monkey’s cognitive behavior in real-time, a key step toward advanced brain-machine interfaces and novel enhancing or therapeutic interventions in the human brain.
Participating labs: Mazzucato, Kiani (at New York University)
Motor Circuit Diagram
NINDS funded project: “Reinforcement learning and action sequencing in subcortical and cortical circuits”
Overview: Learning new behaviors requires the shaping of time-varying patterns of neural activity. In this work, we seek to address how neurons in the cerebral cortex interact with subcortical brain areas to learn things such as selecting actions or how to perform a new motor skill. Understanding how such learning occurs is challenging because multiple brain areas are involved and because such behaviors may involve multiple timescales, from low-level limb movements to the cognitive level of goal-driven planning. By developing models of learning mechanisms in the subcortical and cortical neural circuits that control behavior, using data from experimental collaborators to inform and test these models, and drawing on recent advances in deep learning and artificial intelligence, our research aims to advance understanding of how learned motor behaviors are implemented in the brain.
Participating labs: Murray
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Theoretical Neuroscience Training Program

Applied Math Sequence “Mathematical Foundations of Machine Learning” URL
Overview: The main goals of this three-term sequence are for trainees to gain (a) proficiency with some computational tools central to computational biology and neuroscience, including modeling, computational methods, programming, and translation/communication. This sequence aims to teach these skills in the context of three general and interrelated topics of growing importance: (i) computational modeling and stochastic processes, (ii) machine learning and statistics, and (iii) computation with neural networks.

Theoretical Neuroscience Journal Club
This weekly meeting encourages trainees to approach both recent articles and classic literature on various topics ranging from neural computational and systems neuroscience to deep learning and related fields. Trainees hone their presentation skills and critical judgment in an inclusive and friendly environment. 

Researching Inclusivity in STEM
This course is open to all trainees, faculty and staff and aims at gaining expertise in the areas of: Cultural competency in the classroom; Bias in STEM; Advocacy in STEM, building anti-racist, anti-ableist, and anti-oppressive classrooms. The course features guest speakers with different areas of expertise scheduled throughout the term. Class members will team up and use the literature to build anti-oppressive syllabi, construct diversity statements, and draft grants to improve DEI in our departments at UO.

Computational Neuroscience Workshops
Tutorial on pose tracking (SLEAP) by Talmo Pereira (Salk Institute) 
Tutorial on the Allen Brain Observatory by Gabe Ocker (Boston University) 
Tutorial on state space models by Scott Linderman (Stanford) 

 

Applicants to the Theoretical Neuroscience Graduate Program should apply through the Interdepartmental Neuroscience Graduate Program (INGP). Specifically, the Theoretical Neuroscience Graduate Program is part of the Systems, Cognitive and Theoretical track of the INGP. Students apply to the INGP through the departments of biology, human physiology, psychology, mathematics, physics, computer and information science, and the Knight Campus.

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