Computational Biology and Modeling

The areas of Computational Biology and Modeling being addressed by researchers in the MCIN program include modeling at a wide range of levels, including the electrophysiology of neurons, fMRI activity during decision making, and electroencephalography during various mental tasks.  MCIN investigators that work in these areas include SegerPartinand Anderson.


The Seger lab uses modeling, in particular reinforcement learning models, to quantify information acquisition and feedback processing during learning and relate these measures to brain activation measured using fMRI.  We also use computational techniques to examine interactions between neural regions (e.g., Granger causality modeling), and characterize distributed patterns of brain activity (e.g., multi-voxel pattern analysis)

Seger CA, Peterson E, Lopez-Paniagua D, Cincotta CM, Anderson CM. Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling. NeuroImage.(in press)


Anderson and students have modeled CA1 and CA3 hippocampal neurons to explore hypotheses of epileptiform genesis.

Bush K, Knight J, Anderson C. Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural networks 18, 488-496 (2005).

Bush K, Knight J, Anderson C. Optimizing neural model templates using covariance matrix adaptation and fourier analysis.  Proc Int Joint Conf Neural Networks, July 2005, Montreal, Quebec.

 

Anderson’s lab also studies ways of recognizing patterns in electroencephalography from humans performing various mental tasks.  The goal of this work is a practical brain-computer interface with which disabled persons can control devices like computers or a wheelchair.

Teli MN, Anderson CW. Nonlinear dimensionality reduction of electroencephalogram (EEG) for brain computer interfaces. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, Sept. 2-6 (2009).

Anderson CW, Bratman JA. Translating thoughts into actions by finding patterns in brainwave. In Proceedings of the Fourteenth Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, June 2008, pp. 1-6.  

Anderson CW, Kirby MJ, Hundley D, Knight JN. Classification of time-embedded EEG using short-time principal component analysis.  In Towards Brain-Computer Interfacing ,edited by G. Dornhege, J. del R. Millan, T. Hinterberger, D.J. McFarland, and K.-R. Muller, The MIT Press, pp. 261-278 (2007). 


The Partin lab uses kinetic modeling to study the biophysics of the gating of ion channels, typically, the glutamate-gated AMPA receptors. We useFastFlow software to perform Monte Carlo simulations of gating transitions, and then model how those simulated electrophysiological responses change when allosteric modulators that slow specific rate constants are added to the state diagram.

Partin KM, Fleck MW, Mayer ML. AMPA receptor flip/flop mutants affecting deactivation, desensitization, and modulation by cyclothiazide, aniracetam, and thiocyanate. J Neurosci16, 634-6647 (1996).   Jin R, Clark S, Weeks AM, Dudman J, Gouaux E, Partin KM.  Molecular mechanism of positive allosteric modulators acting on AMPA receptor. J Neurosci25, 9027-9036 (2005).