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 Computational Neuroscience Center
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The Computational Neuroscience Center is dedicated to combining
biophysically inspired neural modeling, and rigorous statistical
techniques with the myriad neural imaging technologies available at
the Martinos Center in order to enhance the understanding of neural
function. The core tenet of the group is that neuroscientific theory
and experiment should be performed in tandem, with each informing, and
benefiting from the insights of, the other. Group members come from
backgrounds as diverse as physics, mathematics, computer science,
statistics, engineering, neuroscience and clinical neurology. Our
group is exceptional in that many of the individual members are
trained both as theorists and experimentalists.
At the Martinos Center, we have access to both world-class imaging
technologies, as well as cutting-edge computational resources.
These tools tools allow us to both develop and implement new
models in computational neuroscience and then obtain corroborating
evidence with non-invasive imaging.
In addition, our
unique position within MGH allows us access to clinical populations
from which we can enhance our understanding of neural
pathologies. Current collaborations include:
- Biophysically modeling the neural generation of EEG/MEG current dipoles
- Development of statistical methods suitable for multiple spike train analysis
- Modeling the cortical hemodynamic response and its generation by neural activity
- Analyzing, using synchrony and Granger causality, the communication between different cortical regions
- Quantifying the representation and transformation of tactile stimuli in the rat somatosensory pathway using information theoretic aproaches
- Clarifying the influence of internally generated cortical states
on the neural response to sensory stimuli
- Acutely predicting tissue infarction using neuroimaging in
stroke patients
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