This three-week mini-program will bring together scientists from computational neuroscience, cognitive neuroscience, and animal neurophysiology and neuroanatomy, to address the common goal of developing a robust framework for unraveling how networks solve problems that allow for rich, contextually significant behavior, scaled to each organism’s neural complexity. A parallel goal is to identify opportunities for bio-inspired applications for technological networks.  Salient problems include decision-making, goal-directed behavior, and adaptation.

New and combined methods for brain imaging and direct recording of cortical and subcortical neuronal activity suggest the possibility of increased spatial and temporal resolution in human studies, and motivate the development of quantitative methods for analysis that will accurately represent dynamics, structure, and uncertainty. Increased resolution in animal experiments yield direct insight into real circuits and anatomy, which provide a potential path to identifying network structure that may help constrain models and human studies. Advances in understanding of dynamics and feedback on artificial networks and relationships between architecture, function, and robustness, suggest new opportunities for theory and modeling neural processes, and development of bio-inspired machine algorithms.

• Week One will consist of tutorials and the identification of open questions suited to synergies represented by the group. Topics include: anatomy, imaging, architecture, dynamic and control, robustness and adaptation.

• Week Two will focus on fundamental processing challenges, including: decision-making, motor control, navigation, search and recognition, learning and adaptation. We will emphasize: (i) cases in which there are closely analogous animal and human studies; (ii) incorporation of constraints from functional modules for processing and physiology in modeling cognitive processes; (iii) identifying opportunities for developing machine systems and algorithms based on biological insights;  (iv) opportunities for developing robust, quantitative methods in areas which include feedback and dynamics, network architecture and function, multivariable data inversion, and rigorous bounds on uncertainty.

• Week Three will focus on resilience and retention, focusing on how the brain and artificial networks develop and robustly retain properties in spite of environmental challenges. The emphasis is on impacts of temporal change over long time scales, including aging, injury and experience.  Topics include: development, novices vs. experts, disease states, and aging. We focus on how functionality is maintained (or enhanced) in the presence of processes that involve time evolution, as well as issues related to individual differences and diversity within a population.