RT Journal Article
SR Electronic
T1 Could a neuroscientist understand a microprocessor?
JF bioRxiv
FD Cold Spring Harbor Laboratory Press
DO 10.1101/055624
A1 Jonas, Eric
A1 Kording, Konrad
YR 2016
UL http://biorxiv.org/content/early/2016/05/26/055624.abstract
AB There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current approaches in neuroscience may fall short of producing meaningful models of the brain.