Fellow Shashaank Vattikuti and I have been exploring how genetic and anatomical perturbations can give rise to conditions such as mental illness and overeating. In particular, we used a local cortical circuit at the sub-millimeter level as a bridge between genotype and phenotype. We have found that synaptic imbalance and changes in the mini-column structure of cortex can effect performance in visual saccade tasks that match experiments. The model also makes predictions for possible pharmacological therapeutics. We are now collaborating with Steve Gotts and Alex Martin to develop psychophysical tests to probe cortical circuit dynamics. Cognitive phenomena that may be usefully exploited to probe cortical circuit function involve visual illusions such as binocular rivalry, where each eye is presented with a different image and the brain's perception alternates between the two images. For the past two decades, I have been developing a physiologically-based cortical circuit model that can account for a comprehensive set of psychophysical and electrophysiological data pertaining to many neural phenomena including perceptual rivalry, stimulus disambiguation, and neural activity normalization. The model is quantified by a small number of parameters that can be mapped back to molecular and genetic sources. We have made progress towards our goal of designing a suite of cognitive tasks where performance measurements combined with neural recordings from MEG and fMRI can be combined to estimate the parameters, which would then give an objective measure or nosology for cognitive function and mental illness. These parameters could be used for defining illnesses, quantifying progression of illnesses, quantifying the effects of treatments and medication. For example, illnesses could be defined in terms of deviations from the mean and treatments could be assessed in terms of how they affected the parameters. The parameters could be used in personalized medicine for treatments of diseases by determining optimal drugs to use, dosing regimens, and drug combinations. They could also be used to help design novel therapies. This could work could lead to a new paradigm for the diagnosis and treatment of diseases.
| Vattikuti, Shashaank; Thangaraj, Phyllis; Xie, Hua W et al. (2016) Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory. PLoS Comput Biol 12:e1004903 |
| Chow, Carson C; Buice, Michael A (2015) Path integral methods for stochastic differential equations. J Math Neurosci 5:8 |
| Buice, Michael A; Chow, Carson C (2013) Dynamic finite size effects in spiking neural networks. PLoS Comput Biol 9:e1002872 |
| Buice, Michael A; Chow, Carson C (2013) Generalized activity equations for spiking neural network dynamics. Front Comput Neurosci 7:162 |
| Buice, Michael A; Chow, Carson C (2011) Effective stochastic behavior in dynamical systems with incomplete information. Phys Rev E Stat Nonlin Soft Matter Phys 84:051120 |
| Seely, Jeffrey; Chow, Carson C (2011) Role of mutual inhibition in binocular rivalry. J Neurophysiol 106:2136-50 |
| Buice, Michael A; Cowan, Jack D; Chow, Carson C (2010) Systematic fluctuation expansion for neural network activity equations. Neural Comput 22:377-426 |
| Vattikuti, Shashaank; Chow, Carson C (2010) A computational model for cerebral cortical dysfunction in autism spectrum disorders. Biol Psychiatry 67:672-8 |
