A new nonlinear dynamical analysis is applied to complex behavior from neuronal systems. The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of 'unstable periodic orbits' (UPOs). UPOs are rigorously identified in data sets representative of three different levels of organization in mammalian brain. An analysis based on UPOs affords a novel alternative method of decoding, predicting, and controlling these neuronal systems.
Bibliographical noteFunding Information:
This work was supported by U.S. National Institutes of Mental Health grant 1-R29-MH50006-05 and 1KO2MH01493-01, U.S. Office of Naval Research grant N0014-95-1-0138, and the U.S. Department of Energy through subcontract 85X-SX516V with Oak Ridge National Laboratory.