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We find that many measures are surprisingly similar between the brain and the processor but that our results do not lead to a meaningful understanding of the processor. For each of these, we will use standard techniques that are popular in the field of neuroscience. To do so, we will analyze the connections on the chip, the effects of destroying individual transistors, single-unit tuning curves, the joint statistics across transistors, local activities, estimated connections, and whole-device recordings. We want to see what kind of an understanding would emerge from using a broad range of currently popular data analysis methods. Here we will try to understand a known artificial system, a classical microprocessor by applying data analysis methods from neuroscience.
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The microprocessors in early computing systems can serve this function. 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. 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?”. 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. However, there are other systems, in particular man made ones that we do understand. In neuroscience it can be difficult to evaluate the quality of a particular model or analysis method, especially in the absence of known truth. It is hard to evaluate how much scaling these techniques will help us understand the brain. However, even state-of-the-art neuroscientific studies are still quite limited in organism complexity and spatiotemporal resolution. Scientists are beginning to reconstruct connectivity, record activity, and simulate computation at unprecedented scales. The development of high-throughput techniques for studying neural systems is bringing about an era of big-data neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. KPK is supported by the National Institutes of Health (MH103910, NS074044, EY021579). įunding: EJ is supported in part by NSF CISE Expeditions Award CCF-1139158, DOE Award SN10040 DE-SC0012463, and DARPA XData Award FA-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. 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.ĭata Availability: All data are available at. Received: SeptemAccepted: NovemPublished: January 12, 2017Ĭopyright: © 2017 Jonas, Kording. University College London, UNITED KINGDOM 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.Ĭitation: Jonas E, Kording KP (2017) Could a Neuroscientist Understand a Microprocessor? PLoS Comput Biol 13(1):
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This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. 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. 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. 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. 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.