Could a Neuroscientist Understand a Microprocessor?

By Eric Jonas, Konrad Paul Kording. bioRxiv, 2016

308 citations recorded by the source corpus.

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 na•vely, fall short of producing a meaningful understanding.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d911c0a03dc483d90afcac15ade24a50fc141e9a

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Citations in the corpus

Toward an Integration of Deep Learning and Neuroscience (2016, background): “Due to the complexity and variability of the brain, pure “bottom up” analysis of neural data faces potential challenges of interpretation (Robinson, 1992; Jonas and Kording, 2016).”
Neuroscience Needs Behavior: Correcting a Reductionist Bias. (2017, unknown): “That said, the recent study by Jonas and Kording (Jonas and Kording, 2017) we referred to earlier provides an empirical demonstration of the fundamental difference between intervening and recording versus understanding how information flows through processing steps.”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “…learning rule, training set, and so on; we cannot ignore the training sample or the computational motifs that yield the embedding space if we hope to understand how the neural network works (for related arguments, see Jonas and Kording, 2017; Lillicrap and Kording, 2019; Richards et al., 2019).”
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017, background): “” Statistical 187 methods that shed light on the function of RNNs and other complex computational models are therefore of great 188 interest [49, 50].”
Deep neuroethology of a virtual rodent (2019, unknown): “(Jonas & Kording, 2017).”
Neuroscience-Inspired Artificial Intelligence. (2017, methodology): “…encourage AI researchers to use approaches from neuroscience to explore properties of network architectures and agents through analysis, visualization, causal manipulation, not forgetting the need for carefully designed hypothesis-driven experiments (Jonas and Kording, 2017; Krakauer et al., 2017).”