On simplicity and complexity in the brave new world of large-scale neuroscience.
Technological advances have dramatically expanded our ability to probe multi-neuronal dynamics and connectivity in the brain. However, our ability to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of theoretically principled data analytic procedures, as well as theoretical frameworks for how circuit connectivity and dynamics can conspire to generate emergent behavioral and cognitive functions. We review and outline potential avenues for progress, including new theories of high dimensional data analysis, the need to analyze complex artificial networks, and methods for analyzing entire spaces of circuit models, rather than one model at a time. Such interplay between experiments, data analysis and theory will be indispensable in catalyzing conceptual advances in the age of large-scale neuroscience.
Department of Bioengineering, Stanford University, Stanford, CA 94305, United States. Electronic address: prgao@stanford.edu.
Peiran
P
Gao
Department of Applied Physics, Stanford University, Stanford, CA 94305, United States.
Surya
S
Ganguli
D000818:Animals; D066264:Datasets as Topic; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009488:Neurosciences / Q000379:methods*