Robert Haslinger, PhD
Instructor in Radiology at Harvard Medical School
Assistant in Neuroscience at Massachusetts General Hospital
Department of Radiology, MGH
PhD, University of Wisconsin
Building 149, Room 2301
13th Street
Charlestown, MA 02129
Phone:
Fax: 617-726-7422
Location:
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DESCRIPTION OF WORK / BIOSKETCH
I'm a physicist currently working in the field of computational neuroscience. My fundamental interest is how the collective, emergent dynamics of neurons in the brain gives rise to complex information processing. Although indiviudal neurons are very noisy, organisms can
reliably perceive sensory stimuli and quickly compute appropriate
behavior to ensure their survival. Such
robust computation relies upon the
coordinated, collective activity of many neurons passing information
back and forth. Recent experimental advances have allowed hundreds, and
presumably soon, thousands or more neurons to be simultaneously
recorded, providing a new opportunity to study how large neuronal
networks coordinate their member neurons activity. Yet it is not
currently clear, which aspects of such network activity are
computationally relevant or how to tease out, from noisy, undersampled
data, the dynamic, functional structures relevant for cognitive processing.
Thus the present challenges in
examining such complex data are twofold. First the development of
new computational tools that can detect structured patterns in large data sets of recorded neural activity. Second, to develop new theories and hypotheses
for how recurrent spiking networks might compute so that we know what
types of structure we're looking for. Towards the first goal I use
techniques from data science (statistics and machine learning) to
develop practical analysis methodologies that can extract, from neural
activity recorded in behaving animals, the collective dynamics that
large networks use to process information. In collaboration with
co-PIs who perform animal experiments, I have applied these methods to
a wide variety of neural systems to determine how real neuronal
networks compute. Towards the second goal I use methods from complex systems and statistical physics to research theoretical models of information
processing and computation by complexly structured neural networks.
Such work aims to provide a theoretical framework to identify and
explain common computational principles underlying apparently diverse
cortical areas and functions.
LINKS
personal research page
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