A provocative new think piece asks whether the experimental neuroscience program is on track by asking the question, "Could a neuroscientist understand a microprocessor?"
It finds that while our current neuroscience research agenda is broad enough, carried to its logical conclusion, to tell us a great deal, that it has serious blind spots that make it incapable of reaching key insights into the functioning of our brains with any amount of data collected in the current paradigm, no matter how much we gather. Therefore, neuroscience needs to supplement its research agenda with new approaches calculated to gain insight into qualitatively different kinds of knowledge about the brain.
There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information.
These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a 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. 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.
Eric Jons and Konrad Kording, "Could a neuroscientist understand a microprocessor?" (Pre-Print November 14, 2016). doi: http://dx.doi.org/10.1101/055624We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.