Could a Neuroscientist Understand a Microprocessor?
Could a Neuroscientist Understand a Microprocessor?
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DOI：
10.1371/journal.pcbi.1005268
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发表时间：
2017-01
影响因子：
4.3
通讯作者：
Kording KP
中科院分区：
文献类型：
--
作者：
Jonas E;Kording KP
神经科学领域有一种流行的观点认为，我们主要受数据限制，并且在先进数据分析算法的帮助下，生成大型、多模态和复杂的数据集将使我们对大脑处理信息的方式有根本性的认识。这些数据集目前还不存在，即便存在，我们也无法评估由算法生成的认识是否充分，甚至是否正确。为了解决这个问题，在此我们将一个经典微处理器作为模式生物，并利用我们对其进行任意实验的能力，来检验神经科学中流行的数据分析方法是否能够阐明它处理信息的方式。微处理器是那些既复杂又能在各个层面（从整体逻辑流程，经过逻辑门，到晶体管的动态）被我们理解的人工信息处理系统之一。我们表明，这些方法揭示了数据中有趣的结构，但并不能有意义地描述微处理器中信息处理的层次结构。这表明，无论数据量有多少，神经科学当前的分析方法可能都无法对神经系统产生有意义的理解。此外，我们主张科学家使用具有已知基本事实的复杂非线性动力系统，比如将微处理器作为时间序列和结构发现方法的验证平台。
神经科学因难以评估结论是否正确而受阻；所研究系统的复杂性以及实验上的难以触及性，使得对算法和数据分析技术的评估充其量也具有挑战性。因此，我们主张使用已知人造物来测试方法，因为其正确解释是已知的。在此我们将一个微处理器平台作为这样一个测试案例。我们发现，神经科学中的许多方法在简单使用时，都无法产生有意义的理解。
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. 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.