On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience
arXiv:1503.08779
Abstract
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.
11 pages, 3 figures
References in corpus (5)
- Deep Learning in Neural Networks: An Overview
- Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
- Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
- Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light
- Function Constrains Network Architecture and Dynamics: A Case Study on the Yeast Cell Cycle Boolean Network