- Title: Associate Professor
- Group: Cellular and Molecular Foundations of Intelligent Behavior
- Status: Beckman Full-time Faculty
- Home: Molecular and Integrative Physiology
- 4165 Beckman Institute
- 405 North Mathews Avenue
- Urbana, Illinois 61801
Tom Anastasio received his Ph.D. from the University of Texas at Galveston in 1986. He is an associate professor in the U of I Department of Molecular and Integrative Physiology and a full-time faculty member in the Beckman Institute NeuroTech group. His field of professional interest is computational neuroscience.
James E. Heath Award for Excellence in Teaching, University of Illinois (1997-1998); James E. Beall II Memorial Award, University of Texas at Galveston (1986).
Experimental neuroscience has produced a tremendous wealth of data concerning the neural substrates of behavior. Professor Anastasio's main goal in science is to create computational models of neural systems that synthesize and explain this data. His hope is that, in so doing, he can gain insight into how the nervous system works.
Anastasio is interested in modeling neural systems that are critical for perception and action. His two main interests are the processing of multisensory input by the superior colliculus, and the adaptive control of movement by the cerebellum. Because organisms experience the world not through one sense but through many, understanding multisensory processing is essential to understanding sensory perception. The superior colliculus is the first major site of multisensory convergence in the mammalian brain. Anastasio has developed models, based on probability theory and neural networks, which offer functional and mechanistic explanations for the multisensory response properties of neurons in the superior colliculus.
Because organisms exist in a changing world, understanding the adaptive control of movement is essential to understanding action. The cerebellum is the main site of adaptive movement control in the vertebrate brain. While the cerebellum seems simple in terms of its neural structure, data on the behavior of cerebellar neurons has been difficult to interpret. Current models present a conflicting picture of cerebellar function. Anastasio has developed new models, based on pattern correlation and neural network learning algorithms, which unify the various views of cerebellar adaptation.
An exciting avenue for collaborative work currently underway at the Institute involves the application of some of these models in the design of brain-like devices. Models of multisensory processing are being applied in the design of a self-aiming camera. Models of cerebellar adaptation could find application in robot control systems.
Anastasio's work fits into the themes of Biological Intelligence and Human-Computer Intelligent Interaction, since it involves a study of the properties of neurobiological computers.
His sources of funding include the NSF and ONR.
Anastasio, T. J., Temporal-Logic Analysis of Microglial Phenotypic Conversion with Exposure to Amyloid-Beta. Molecular Biosystems 2015, 11, (2), 434-453, DOI:10.1039/C4mb00457d.
Anastasio, T. J., Computational Identification of Potential Multitarget Treatments for Ameliorating the Adverse Effects of Amyloid-Beta on Synaptic Plasticity. Frontiers in Pharmacology 2014, 5, DOI:Artn 85 DOI:10.3389/Fphar.2014.00085.
Anastasio, T. J., Computational Search for Hypotheses Concerning the Endocannabinoid Contribution to the Extinction of Fear Conditioning. Frontiers in Computational Neuroscience 2013, 7, 17, DOI: 10.3389/fncom.2013.00074.
Anastasio, T. J., Exploring the Contribution of Estrogen to Amyloid-Beta Regulation: A Novel Multifactorial Computational Modeling Approach. Frontiers in Pharmacology 2013, 4, 16.
Ma, R.; Cui, H.; Lee, S. H.; Anastasio, T. J.; Malpeli, J. G., Predictive Encoding of Moving Target Trajectory by Neurons in the Parabigeminal Nucleus. Journal of Neurophysiology 2013, 109, (8), 2029-2043
Anastasio, T. J.; Ehrenberger, K.; Watson, P.; Zhang, W. Individual and Collective Memory Formation: Analogous Processes on Different Levels; MIT Press: Cambridge, MA, 2012, Arch, V. S.; Simmons, D. D.; Quinones, P. M.; Feng, A. S.; Jiang, J. P.; Stuart, B. L.; Shen, J. X.; Blair, C.; Narins, P. M., Inner Ear Morphological Correlates of Ultrasonic Hearing in Frogs. Hearing Research 2012, 283, (1-2), 70-79.
Anastasio, T. J., Data-Driven Modeling of Alzheimer Disease Pathogenesis. Journal of Theoretical Biology 2011, 290, 60-72.
Gad, Y. P.; Anastasio, T. J., Simulating the shaping of the fastigial deep nuclear saccade command by cerebellar Purkinje cells. Neural Networks 2010, 23, (7), 789-804, doi:10.1016/j.neunet.2010.05.007.
Rothganger, F. H.; Anastasio, T. J., Using input minimization to train a cerebellar model to simulate regulation of smooth pursuit. Biological Cybernetics 2009, 101, (5-6), 339-359.
Barreiro, A. K.; Bronski, J. C.; Anastasio, T. J., Bifurcation theory explains waveform variability in a congenital eye movement disorder. Journal of Computational Neuroscience 2009, 26, (2), 321-329.
Raginsky, M.; Anastasio, T. J., Cooperation in self-organizing map networks enhances information transmission in the presence of input background activity. Biological Cybernetics 2008, 98, (3), 195-211.
Anastasio, T. J.; Gad, Y. P., Sparse cerebellar innervation can morph the dynamics of a model oculomotor neural integrator. Journal of Computational Neuroscience 2007, 22, (3), 239-254.
Anastasio, T.J.; Patton, P.E. A Two-stage Unsupervised Learning Algorithm Reproduces Multisensory Enhancement in a Neural Network Model of the Corticotectal System. Journal of Neuroscience 2003, 23, 6713-6727.
Patton, P.E.; Anastasio, T.J. Modeling Cross-modal Enhancement and Modality-specific Suppression in Multisensory Neurons. Neural Computation 2003, 15, 783-810.
Patton, P.E.; Belkacem-Boussaid, K.; Anastasio, T.J. Multimodality in the Superior Colliculus: An Information Theoretic Analysis. Cognitive Brain Research 2002, 14, 10-19.
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