Introduction to The Research

• Analysis of a number of important human brain circuits has led to specific and distinct hypotheses of their functions. Simulations at various levels of simplification and abstraction have demonstrated substantial computational power and efficiency of the hypothesized operations.

• Extensive mathematical characterization of these functions, and comparison with related algorithms have been performed and published in scientific journals.

• Many of the resulting derived algorithmic methods have led to applications in military, commercial, and medical domains.

• Specific anatomical, physiological, and behavioral predictions have been tested in animal and human experiments, with results that support the growing body of hypotheses.

• Further construction of computational simulation systems, more extensive mathematical analyses, and continued empirical testing of biological and behavioral predictions, are ongoing in our laboratory.

Perceptual geometry 

• Rundle M, Coch D, Connolly A, Granger R (2018) Dissociating frequency and animacy effects in visual word processing. Brain and Language 183:54-63.

• Rodriguez A, Granger R (2017) The differential geometry of perceptual similarity [arXiv:1708.00138v1] doi: 10.3758/ s13423-014-0653-y

 Formal analysis and applications 

• Lee YS, Peelle J, Kraemer D, Lloyd S, Granger R (2015) Multivariate senstivity to voice during auditory categorization. Journal of Neurophysiology. 114(3): 1819-26. nih
• Rodriguez A, Granger R (2016) The grammar of mammalian brain capacity. Theoretical Computer Science C (TCS-C): 633:100-111. doi: 10.1016/j.tcs.2016.03.021 TCS
• Bowen E, Tofel B, Parcak S, Granger R (2017) Algorithmic identification of looted archaelogical sites from space. Frontiers in Information and Communication Technology (ICT), 4:4 doi: 10.3389/fict.2017.00004 F.IC

Integration across modalities: vision and sound 

• Chandrashekar A, Torresani L, Granger R (2013) Learning what is where from unlabeled images: joint localization and clustering of foreground objects. Machine Learning, 94: 261-279. doi: 10.1007/s10994-013-5330-2
• Fogelson S, Kohler P, Miller K, Granger R, Tse P (2014) Unconscious neural processing differs with method used to render stimuli invisible. Frontiers in Psychol., 5: 601. doi: 10.3389/fpsyg.2014.00601
• Lee Y, Janata P, Frost C, Martinez Z, Granger R (2014) Melody recognition revisited: influence of melodic gestalt on the encoding of relational pitch information. Psychonomic Bulletin & Review, 1-7. doi: 10.3758/ s13423-014-0653-y

Real-world seeing and hearing: Neuroimaging and fielded applications 

• Lee Y, Janata P, Frost C, Hanke M, Granger R (2011) Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. NeuroImage, doi: 10.1016/j.neuroimage.2011.02.006.
• Lee Y, Turkeltaub P, Granger R, Raizada R (2012) Categorical speech processing in Broca's area: An fMRI study using multivariate pattern-based analysis. J Neurosci., 32: 3942-3948. J Neurosci
• Chandrashekar A, Granger R (2012) Derivation of a novel efficient supervised learning algorithm from cortical-subcortical loops. Frontiers Comput Neurosci., 5:50. doi: 10.3389/fncom.2011.00050

Integration: circuits, systems, neuroimaging 

• Felch A, Granger R (2010) Sensor-rich robots driven by real-time brain circuit algorithms. In: Neuromorphic and brain-based robots (Krichmar & Wagatsuma, Eds)
• Dhulekar N, Felch A, Granger R (2010) Tracking moving objects improves recognition. Int’l Conf on Image Processing, Computer Vision, & Pattern Recognition (IPCV), pp.798-803.
• Granger R (2011). How brains are built: Principles of computational neuroscience. Cerebrum; The Dana Foundation. http://dana.org/news/cerebrum/detail.aspx?id=30356

Evolution of real and artificial brains 

• Lynch G, Granger R (2008) Big Brain: The origins and future of human intelligence. Palgrave Macmillan.
• Hearn R, Granger R (2009) Learning hierarchical representations and behaviors. In: Symposium on Naturally- Inspired Artificial Intelligence, American Association for Artificial Intelligence (AAAI).
• Moorkanikara J, Felch A, Chandrashekar A, Dutt N, Granger R, Nicolau A, Veidenbaum A. (2009) Brain-derived vision algorithm on high-performance architectures. Int’l Journal of Parallel Prog., 37: 345-369.

Building a brain 

• Granger R (2006) Engines of the brain: The computational instruction set of human cognition. AI Magazine 27: 15-32.
• Granger R (2006) Essential circuits of cognition: The brain's basic operations, architecture, and representations. In: AI at 50; The future of Artificial Intelligence. (J.Moor, G.Cybenko, Eds.)
• Granger R (2006) The evolution of computation in brain circuitry. Behav. Brain Sci 29: 17-18.
• Felch A, Granger R (2007) The hypergeometric connectivity hypothesis: Divergent performance of brain circuits with different synaptic connectivity distributions. Brain Research (In press).

Integrative modeling of thalamo-cortico-striatal processing 

• Rodriguez A, Whitson J, Granger R (2004) Derivation and analysis of basic computational operations of thalamocortical circuits. J. Cognitive Neurosci, 16: 856-877.
• Granger R, Petrovic S, Felch A, Kerr J, Johnson M, Wuerth C, Benvenuto J. (2004) Engines of the brain: The computational instruction set of perception and cognition. MIT AAAI Symposium (P.Winston, Ed). CA: AAAI Press.
• Granger R. (2005) Brain circuit implementation: High-precision computation from low-precision components. In:Replacement Parts for the Brain (T.Berger,Ed) MA: MIT Press.

Benzothiadiazides: A new family of glutamate receptor compounds 

• Phillips, D, Sonnenberg, J, Arai, A, Vaswani, R, Krutzik, P, Kleisli, T, Kessler, M, Granger, R, Lynch, G, Chamberlin, R (2002). 5'-Alkyl-benzothiadiazides: A new subgroup of AMPA receptor modulators with improved affinity. Bioorganic & Medicinal Chem. 10: 1229-1248.
• Arai, A, Yan-Fang, X, Kessler, M, Phillips, D, Chamberlin, R, Granger, R, Lynch, G. (2002). Effects of 5'-Alklyl-Benzothiadiazides on AMPA receptor biophysics and synaptic responses. Molecular Pharmacology, 62: 566-577.

Clinical demonstration of EEG-based diagnostic in Alzheimer's patients 

• Granger, R. (2001). NeuroGraph Evoked Potential System. Food and Drug Administration (FDA) 510(k) clearance #K010669.
• Benvenuto J, Jin Y, Casale M, Lynch G, Granger R (2002) Identification of diagnostic evoked response potential segments in Alzheimerˆïs Disease. Exper. Neurology, 176: 269-276.

 Novel brain activity assay based on in-situ hybridization of immediate-early genes (IEGs) 

• Lynch, G., Granger, R., Gall, C., Palmer, L. (2000). Assay for determination of neuronal activity in brain tissue. U.S. Patent # 5,998,139 (6 claims).
• Hess U, Granger R, Lynch G, Gall C (1997) Differential patterns of c-fos mRNA expression in amygdala during successive stages of odor discrimination learning. Learning & Memory 4: 262-283.

Novel method for 2-D current source density analysis for localization of electrophysiological activity 

• Shimono K, Brucher F, Granger R, Lynch G, Taketani M (2000) Origins and distribution of cholinergically induced beta rhythms in hippocampal slices. J. Neuroscience 20: 8462-8473.
• Shimono, K., Taketani, M., Brucher, F., Kubota, D., Colgin, L., Robertson, S., Granger R., Lynch, G. (2001). Continuous two-dimensional current-source density analyses of electrophysiological activity in hippocampal slices. Neurocomputing 38: 899-905.

Novel methods for EEG-based neurological diagnostics 

• Granger R (2001) Method and computer program product for assessing neurological conditions and treatments using evoked response potentials. U.S. Patent # 6,223,074 (54 claims).
• Granger, R., and Lynch, G. (2001). Method and apparatus for assessing susceptibility to stroke. U.S. Patent # 6,280,393 (17 claims).

Human and animal studies demonstrating amelioration of age-related deficits via glutamatergic receptor modulation

• Granger, R., Deadwyler, S., Davis, M., Moskowitz, B., Kessler, M., Rogers, G., and Lynch, G. (1996). Facilitation of glutamate receptors reverses an age-associated memory impairment in rats. Synapse, 22: 332-337.
• Lynch, G., Granger, R., Davis, M., Ambros-Ingerson, J., Kessler, M., Schehr, R. (1997). Evidence that a positive modulator of glutamate receptors improves recall in elderly human subjects. Experimental Neurology, 145: 89-92
• Ingvar M, Ambros J, Davis M, Granger R, Kessler M, Rogers G, Schehr R, Lynch G. (1997). Enhancement by an ampakine of memory encoding in humans. Exper Neurol, 146: 553-559.

Derivation of advanced temporal sequence algorithms, and Bayes classification methods, from brain circuits 

• Granger R, Whitson J, Larson J, Lynch G (1994) Non-Hebbian properties of LTP enable high-capacity encoding of temporal sequences. Proc. Nat'l. Acad. Sci., 91: 10104-10108.
• Coultrip, R. and Granger, R. (1994). LTP learning rules in sparse networks approximate Bayes classifiers via Parzen's method. Neural Networks, 7: 463-476.

Modeling of hippocampal fields CA1, CA3, dentate, and entorhinal cortex 

• Granger R, Wiebe S, Taketani M, Ambros-Ingerson J, Lynch G. (1996). Distinct memory circuits comprising the hippocampal region. Hippocampus, 6: 567-578.
• Kilborn, K., Lynch, G., and Granger, R. (1996). Effects of LTP on response selectivity of simulated cortical neurons. J. Cognitive Neurosci., 8: 328-343.

Network modeling of excitatory-inhibitory physiological interaction in cortex 

• Coultrip, R., Granger, R., and Lynch, G. (1992). A cortical model of winner-take-all competition via lateral inhibition. Neural Networks, 5: 47-54.
• Anton, P., Granger, R., and Lynch, G. (1993). Simulated dendritic spines influence reciprocal synaptic strengths and lateral inhibition in the olfactory bulb. Brain Res., 628: 157-165.

Identifcation of 'ampakines': a novel class of centrally-active compounds that act at ampa-type glutamate receptors, which are the most numerous neurotransmitter receptor type in the human brain 

• Granger, R., Staubli, U., Davis, M., Perez, Y., Nilsson, L., Rogers, G., and Lynch, G. (1993). A drug that facilitates glutamatergic transmission reduces exploratory activity and improves performance in a learning-dependent task. Synapse, 15: 326-329.

Simulations of olfactory bulb and paleocortex identify novel hypothesis of olfactory perceptual function 

• Ambros-Ingerson, J., Granger, R., and Lynch, G. (1990). Simulation of paleocortex performs hierarchical clustering. Science, 247: 1344-1348.
• Granger, R., and Lynch, G. (1991). Higher olfactory processes: Perceptual learning and memory. Current Opin. Neurosci., 1: 209-214.

Behavioral and electrophysiological predictions tested in freely moving animals 

• Granger, R., Staubli, U., Powers, H., Otto, T., Ambros-Ingerson, J., and Lynch, G. (1991). Behavioral tests of a prediction from a cortical network simulation. Psychol. Sci., 2: 116-118.
• McCollum, J., Larson, J., Otto, T., Schottler, F. Granger, R., and Lynch, G. (1991). Short-latency single-unit processing in olfactory cortex. J. Cognitive Neurosci., 3: 293-299.