Complex Systems

Complex systems research involves studying the spatio-temporal dynamics of units interconnected in networks. The geometry of such networks and the characteristics of the units themselves may change with time, depending on external signals as well as through internal feedback and learning. The brain, the immune system, the stock market and the ecosystem are examples of complex systems. From the study of individual biomolecules, to cell dynamics and signal processing by neuronal networks, and finally relating what we learn at nano and meso-scales to memory, perception and decision-making tasks, gives an indication of the vast range of complex systems research.

Research on complex systems at UMD relevant to brain and behavior is extensive. The dynamics of complex systems has been a long standing area of strength, with recognized world leaders in chaos theory and far from equilibrium dynamics fostering excellence on campus in many ways e.g. by attracting top graduate students, collaborators, and obtaining top rankings in the physics subfield of nonlinear dynamics.  For these groups to impact research on the brain, there is an urgent need for coherence between individual researchers and groups to communicate and focus on significant problems that need interdisciplinary collaborations to be solved.

Such research at the interface of brain science and complex systems science is already starting to take off at UMD: One example is that of UMD researchers who have just received one of the inaugural NIH BRAIN initiative awards. With their three-year $1.7 million grant, the researchers plan to develop new imaging technologies and data analysis techniques that will further our understanding of how large networks of neurons in the brain interact to process sensory information. A second example is recent highly cited theoretical work that interpreted experimentally observed collective behavior of neurons in the framework of network criticality. A third example is a new R01 funded project focused on the development of the hippocampal-memory network during early childhood and its relation to episodic memory ability. A key aspect of the proposed work is that it takes a network perspective on episodic memory development.

These examples involve a combination of detailed experimental observations from cell to brain scales, and complex systems analysis to model and quantitatively interpret the measurements. We see these interactions as just the tip of the iceberg of possibilities, especially in light of the strong emphasis of the BRAIN initiative on new technologies to measure the brain in behavioral contexts. The BBI will be an on-campus network for researchers with the goal of providing the infrastructure, facilities and connections to select and focus on significant problems that we can delineate and solve, that lie at the interface of disciplines now resident in different academic units and colleges.