Most of my research centers on designing computational models of
neural circuits that allow new insights into how neural states give
rise to perception, cognition and behavior. For example, realistic
models of the central visual system have been used to understand
aspects of perceptual organization (Gestalt laws) and of visual
integration across multiple submodalities. Another interest involves
the action of neuromodulators on plastic changes in the activity of
neurons and the efficacy of neural connections. Computational models
of the effects of neuromodulators (such as dopamine or noradrenaline)
can be used to construct systems-level models of learning and memory.
A very important aspect of my work involves embedding simulated nervous
system models in a real-world device that can sense environmental
stimuli and show autonomous behavior. Such devices typically resemble
robotic hardware, but are under neural control and are capable of
learning from experience. It turns out that "embodiment"
of a neural architecture in a real device adds to the capability
and adaptability of the nervous system. I am also interested in
applying concepts from information theory and graph theory to the
analysis of global states of networks, for example in evaluating
the amount of information shared between different subdivisions
of the nervous system. These more statistical and mathehmatical
approaches may be useful in analyzing multidimensional data sets
obtained from neurophysiology or neuroimaging.
Sporns, O., Tononi, G., and Edelman, G.M. (2000). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10: 127-141.
Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., Thelen, E. (2001) Autonomous mental development by robots and animals. Science, 291: 599-600.
Tononi, G., and Sporns, O. (2003). Measuring information integration. BMC Neuroscience, 4: 31.
Sporns, O., and Zwi, J. (2004). The small world of the cerebral cortex. Neuroinformatics, 2: 145-162.
Sporns, O., Chialvo, D., Kaiser, M., and Hilgetag, C.C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8: 418-425.
Sporns, O. and Kotter, R. (2004) Motifs in brain networks. PLoS Biology 2:1910-1918.
Sporns, O., Tononi and Kotter, R. (2005) The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4):e42.
Chadderdon G. and Sporns, O. (2006). A large-scale neurocomputational model of task-oriented selection and working memory in prefrontal cortex. Journal of Cognitive Neuroscience, 18: 242-257.