On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience

TL;DR

This paper discusses the challenges and potential strategies for extracting simple, conceptual understanding from complex large-scale neuroscience data, emphasizing the need for new theories and analytical methods.

Contribution

It highlights the importance of developing principled data analysis procedures and theoretical frameworks to understand emergent brain functions from large-scale neural data.

Findings

Need for high-dimensional data analysis theories

Importance of analyzing artificial neural networks

Analyzing entire model spaces rather than individual models

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.