Could a Neuroscientist Understand a Microprocessor? | PLOS Computational Biology
Article
Authors
Metrics
Comments
Media Coverage
Reader Comments
Figures
Figures
Abstract<br>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. We 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.
Author Summary
Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used naïvely, fall short of producing a meaningful understanding.
Citation: Jonas E, Kording KP (2017) Could a Neuroscientist Understand a Microprocessor? PLoS Comput Biol 13(1):<br>e1005268.
https://doi.org/10.1371/journal.pcbi.1005268<br>Editor: Jörn Diedrichsen,<br>University College London, UNITED KINGDOM
Received: September 18, 2016; Accepted: November 16, 2016; Published: January 12, 2017<br>Copyright: © 2017 Jonas, Kording. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.<br>Data Availability: All data are available at http://ericmjonas.github.io/neuroproc/.<br>Funding: EJ is supported in part by NSF CISE Expeditions Award CCF-1139158, DOE Award SN10040 DE-SC0012463, and DARPA XData Award FA8750-12-2-0331, and gifts from Amazon Web Services, Google, IBM, SAP, The Thomas and Stacey Siebel Foundation, Adatao, Adobe, Apple, Inc., Blue Goji, Bosch, Cisco, Cray, Cloudera, EMC2, Ericsson, Facebook, Fujitsu, Guavus, HP, Huawei, Informatica, Intel, Microsoft, NetApp, Pivotal, Samsung, Schlumberger, Splunk, Virdata, and VMware. KPK is supported by the National Institutes of Health (MH103910, NS074044, EY021579). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.<br>Competing interests: The authors have declared that no competing interests exist.
Introduction<br>The development of high-throughput techniques for studying neural systems is bringing about an era of big-data neuroscience [1, 2]. Scientists are beginning to reconstruct connectivity [3], record activity [4], and simulate computation [5] at unprecedented scales. However, even state-of-the-art neuroscientific studies are still quite limited in organism complexity and spatiotemporal resolution [6–8]. It is hard to evaluate how much scaling these techniques will help us understand the brain.
In neuroscience it can be difficult to evaluate the quality of a particular model or analysis method, especially in the absence of known truth. However, there are other systems, in particular man made ones that we do understand. As such, one can take a human-engineered system and ask if the methods used for studying biological systems would allow understanding the artificial system. In this way, we take as inspiration Yuri Lazbnick’s well-known 2002 critique of modeling in molecular biology, “Could a biologist fix a radio?” [9]. However, a radio is clearly much simpler than the nervous system, leading us to seek out a more complex, yet still well-understood engineered system. The microprocessors in early computing systems can serve this function.
Here we will try to understand a known artificial system, a classical microprocessor by applying data analysis...