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Computational Neuroscience

PNC student Shreejoy Tripathy measures and studies the impact of biophysical diversity. Shown is a rendering of the observed diversity among mitral cells, the main output neurons of the olfactory bulb - each line corresponds to a unique neuron recording, and the set of lines across parameters fully describe each neuron's response to an arbitrary input.

PNC Student Ashok Litwin-Kumar is using large scale mathematical models of cortical networks and theoretical techniques borrowed from statistical mechanics to investigate how the structure of connections between neurons influences dynamical variability in distinct cortical states. Pictured above are graph diagrams of the wiring between neurons in distinct cortical models, and below is pictured the corresponding spiking dynamics of the models.

PNC Student Gustavo Sudre develops cutting edge machine learning analysis to estimate brain activity during language processing. This figure shows the estimated sources of magnetic activity in the brain 265ms after a subject is instructed to think of the word "hand".

 

Bronwyn

This is a snapshot from a procedure PNC student Bronwyn Woods is developing to automatically identify cells (regions of interest) in images resulting from two-photon calcium imaging (left). These pictures correspond to a single scale of a multi-scale blob detector that generates candidate cell outlines which are later classified as true cells or false positives (right).

 

Computational neuroscience brings many ideas and tools associated with computation to the study of the nervous system. Major influences have come from the success of biophysical models of neural activity, the enduring appeal of the brain-as-computer metaphor, and the increasing prominence of statistical and machine learning methods throughout science. Here in Pittsburgh we have an exceptionally large and vibrant community of neuroscientists who develop and/or apply cutting-edge computational methods in their work. We offer a Ph.D. through our Program in Neural Computation (PNC), an undergraduate minor in neural computation, year-long fellowships for CMU and Pitt undergraduates, and a program of summer undergraduate research that draws students from across the U.S. Our research may be described, roughly, as falling into one or more of the following three broad categories:

Modeling of Neurons and Neural Circuits

The synaptic wiring and response properties of biophysically realistic neural networks are extremely complicated, yet they are amenable to both theoretical and experimental investigation. Modeling of neural behavior uses techniques from dynamical systems theory and statistics, with a central goal of elucidating the way information is represented by the diverse patterns of neural spiking activity, which is often labeled neural coding.

System and Cognitive Modeling

System and cognitive models that characterize information processing capabilities of the nervous system aim to further understanding of diverse topics such as sensory coding, memory formation, language processing, visual attention, categorization, problem solving, and object recognition. Theories use reduced frameworks to provide concrete descriptions of the ways large-scale neural activity relates to cognition.

Recording and Analysis of Network Activity

A large number of faculty are interested in collection, analysis, and modeling of large-scale population recording. This creates a reservoir of support for those who want to apply, or get training in, cutting-edge analytical methods. It also produces a broadened notion of computational modeling to include statistical models, which have come to play important roles in contemporary conceptualizations of neural processing.

The computational neuroscience community here at CNBC consists of both faculty whose expertise is primarily computational and those who have expertise in experimental methods as well. Visit the Computational Neuroscience Faculty Directory for contact information.

Those whose expertise is primarily computational include:

  • Brent Doiron (Pitt, Mathematics)
  • Bill Eddy (CMU, Statistics and Machine Learning)
  • Bard Ermentrout (Pitt, Mathematics)
  • Chris Genovese (CMU, Statistics)
  • Satish Iyengar (Pitt, Statistics)
  • Rob Kass (CMU, CNBC, Statistics, and Machine Learning)
  • Pat Loughlin (Pitt, Bioengineering)
  • Tom Mitchell (CMU, Machine Learning)
  • Paul Munro (Pitt, Information Science and Telecommunications)
  • David Plaut (CMU, CNBC and Psychology)
  • Jonathan Rubin (Pitt, Mathematics)
  • Cosma Shalizi (CMU, Statistics)
  • Dave Touretzky (CMU, Computer Science)
  • Valérie Ventura (CMU, Statistics)
  • Byron Yu (CMU, Biomedical and Electrical and Computer Engineering)

Additional faculty whose expertise is both computational and experimental include:

  • John Anderson (CMU, Psychology and Computer Science)
  • Aaron Batista (Pitt, Bioengineering)
  • Marlene Behrmann (CMU, Psychology)
  • Steven Chase (CMU, CNBC and Biomedical Engineering)
  • Marlene Cohen (Pitt, Neuroscience)
  • Neeraj Gandhi (Pitt, Otolaryngology)
  • Avniel Ghuman (Pitt, Neurological Surgery)
  • Charles Kemp (CMU, Psychology)
  • Seong-Gi Kim (Pitt, Radiology)
  • Tai Sing Lee (CMU, CNBC and Computer Science)
  • Steve Meriney (Pitt, Neuroscience)
  • Carl Olson (CMU, CNBC)
  • Anne-Marie Oswald (Pitt, Neuroscience)
  • Lynne Reder (CMU, Psychology)
  • Mark Redfern (Pitt, Bioengineering)
  • Andrew Schwartz (Pitt, Neurobiology)
  • Daniel Simons (Pitt, Neurobiology)
  • Matthew Smith (Pitt, Ophthalmology)
  • Michael Tarr (CMU, CNBC and Psychology)
  • Robert Turner (Pitt, Neurobiology)
  • Timothy Verstynen (CMU, CNBC and Psychology)
  • Nathan Urban (CMU, CNBC and Biology)
  • Wei Wang (Pitt, Physical Medicine and Rehabilitation)
  • Doug Weber (Pitt, Bioengineering and Physical Medicine and Rehabilitation)