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PreprintCited by 329Neural dynamics and brain functionNeural Networks and ApplicationsCognitive Computing and Networks

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Could a Neuroscientist Understand a Microprocessor?

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Jonas, Eric, Paul Kording, Konrad·2016·PLoS Computational Biology
DOI 10.1101/055624PMC PMC5230747
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Abstract

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.

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Fig 1 A microprocessor is understood at all levels. (A) The instruction fetcher obtains the next instruction from memory. This then gets converted into electrical signals by the instruction decoder, and these signals enable and disable various internal parts of the processor, such as registers and the arithmetic logic unit (ALU). The ALU performs mathematical operations such as addition and subtraction. The results of these computations can then be written back to the registers or memory. (B) Within the ALU there are well-known circuits, such as this one-bit adder, which sums two one-bit signals and computes the result and a carry signal. (C) Each logic gate in (B) has a known truth table and is implemented by a small number of transistors. (D) A single NAND gate is comprised of transistors, each transistor having three terminals (E). We know (F) the precise silicon layout of each transistor.
Fig 1 A microprocessor is understood at all levels. (A) The instruction fetcher obtains the next instruction from memory. This then gets converted into electrical signals by the instruction decoder, and these signals enable and disable various internal parts of the processor, such as registers and the arithmetic logic unit (ALU). The ALU performs mathematical operations such as addition and subtraction. The results of these computations can then be written back to the registers or memory. (B) Within the ALU there are well-known circuits, such as this one-bit adder, which sums two one-bit signals and computes the result and a carry signal. (C) Each logic gate in (B) has a known truth table and is implemented by a small number of transistors. (D) A single NAND gate is comprised of transistors, each transistor having three terminals (E). We know (F) the precise silicon layout of each transistor.CC BY · PubMed Central
Fig 2 Optical reconstruction of the microprocessor to obtain its connectome. In [11], the (A) MOS 6502 silicon die was examined under a visible light microscope (B) to build up an image mosaic (C) of the chip surface. Computer vision algorithms were used to identify metal and silicon regions (E) to detect transistors (F), (G) ultimately producing a complete accurate netlist of the processor (D).
Fig 2 Optical reconstruction of the microprocessor to obtain its connectome. In [11], the (A) MOS 6502 silicon die was examined under a visible light microscope (B) to build up an image mosaic (C) of the chip surface. Computer vision algorithms were used to identify metal and silicon regions (E) to detect transistors (F), (G) ultimately producing a complete accurate netlist of the processor (D).CC BY · PubMed Central
Fig 3 Discovering connectivity and cell type. Reproduced from [31]. (A) The spatial distribution of the transistors in each cluster show a clear pattern (B) The clusters and connectivity versus distance for connections between Gate and C1, Gate and C2, and C1 and C2 terminals on a transistor. Purple and yellow types have a terminal pulled down to ground and mostly function as inverters. The blue types are clocked, stateful transistors, green control the ALU and orange control the special data bus (SDB).
Fig 3 Discovering connectivity and cell type. Reproduced from [31]. (A) The spatial distribution of the transistors in each cluster show a clear pattern (B) The clusters and connectivity versus distance for connections between Gate and C1, Gate and C2, and C1 and C2 terminals on a transistor. Purple and yellow types have a terminal pulled down to ground and mostly function as inverters. The blue types are clocked, stateful transistors, green control the ALU and orange control the special data bus (SDB).CC BY · PubMed Central
Fig 4 Lesioning every single transistor to identify function. We identify transistors whose elimination disrupts behavior analogous to lethal alleles or lesioned brain areas. These are transistors whose elimination results in the processor failing to render the game. (A) Transistors which impact only one behavior, colored by behavior. (B) Breakdown of the impact of transistor lesion by behavioral state. The elimination of 1565 transistors have no impact, and 1560 inhibit all behaviors.
Fig 4 Lesioning every single transistor to identify function. We identify transistors whose elimination disrupts behavior analogous to lethal alleles or lesioned brain areas. These are transistors whose elimination results in the processor failing to render the game. (A) Transistors which impact only one behavior, colored by behavior. (B) Breakdown of the impact of transistor lesion by behavioral state. The elimination of 1565 transistors have no impact, and 1560 inhibit all behaviors.CC BY · PubMed Central
Fig 5 Analyzing the spikes to understand their statistics. (A) 10 identified transistors and (B) their spiking (rising edge) behavior over a short time window during behavior DK.
Fig 5 Analyzing the spikes to understand their statistics. (A) 10 identified transistors and (B) their spiking (rising edge) behavior over a short time window during behavior DK.CC BY · PubMed Central
Fig 6 Quantifying tuning curves to understand function. Mean transistor response as a function of output pixel luminance. (A) Some transistors exhibit simple unimodal tuning curves. (B) More complex tuning curves. (C) Transistor location on chip.
Fig 6 Quantifying tuning curves to understand function. Mean transistor response as a function of output pixel luminance. (A) Some transistors exhibit simple unimodal tuning curves. (B) More complex tuning curves. (C) Transistor location on chip.CC BY · PubMed Central
Fig 7 Spike-word analysis to understand synchronous states. (A) Pairs of transistors show very weak pairwise correlations during behavior SI, suggesting independence. (B) If transistors were independent, shuffling transistor labels (blue) would have no impact on the distribution of spikes per word, which is not the case (red).
Fig 7 Spike-word analysis to understand synchronous states. (A) Pairs of transistors show very weak pairwise correlations during behavior SI, suggesting independence. (B) If transistors were independent, shuffling transistor labels (blue) would have no impact on the distribution of spikes per word, which is not the case (red).CC BY · PubMed Central
Fig 8 Examining local field potentials to understand network properties. We recorded from the processor during behavior DK. (A) Transistor switching is integrated and low-pass filtered over the indicated region. (B) local-field potential measurements from the indicated areas. (C) Spectral analysis of the indicated LFP regions identifies varying region-specific oscillations or “rhythms”.
Fig 8 Examining local field potentials to understand network properties. We recorded from the processor during behavior DK. (A) Transistor switching is integrated and low-pass filtered over the indicated region. (B) local-field potential measurements from the indicated areas. (C) Spectral analysis of the indicated LFP regions identifies varying region-specific oscillations or “rhythms”.CC BY · PubMed Central
Fig 9 Analyzing conditional Granger causality to understand functional connectivity. Each of the recordings come from a well defined functional subcircuit. Green and blue are two parts of the decoder circuit. Red includes the status bits. Violet are part of the registers and yellow includes parts of the accumulator. We estimated for each behavioral state from LFP sites indicated in Fig 8. Arrows indicate direction of Granger-causal relationship, arrow thickness indicates effect magnitude.
Fig 9 Analyzing conditional Granger causality to understand functional connectivity. Each of the recordings come from a well defined functional subcircuit. Green and blue are two parts of the decoder circuit. Red includes the status bits. Violet are part of the registers and yellow includes parts of the accumulator. We estimated for each behavioral state from LFP sites indicated in Fig 8. Arrows indicate direction of Granger-causal relationship, arrow thickness indicates effect magnitude.CC BY · PubMed Central
Fig 10 The processor activity map. For each of three behavioral states we plotted all the activities. Each transistor’s activity is normalized to zero-mean and unit variance and plotted as a function of time.
Fig 10 The processor activity map. For each of three behavioral states we plotted all the activities. Each transistor’s activity is normalized to zero-mean and unit variance and plotted as a function of time.CC BY · PubMed Central
Fig 11 Dimensionality Reduction to understand the roles of transistors. We apply non-negative matrix factorization (NMF) to the space invaders (SI) task. (A) shows the six reduced dimensions as a function of time showing clear stereotyped activity. (B) the learned transistor state vectors for each dimension (C) Map of total activity—color indicates the dimension where the transistor has maximum value, and both saturation and point size indicate the magnitude of that value.
Fig 11 Dimensionality Reduction to understand the roles of transistors. We apply non-negative matrix factorization (NMF) to the space invaders (SI) task. (A) shows the six reduced dimensions as a function of time showing clear stereotyped activity. (B) the learned transistor state vectors for each dimension (C) Map of total activity—color indicates the dimension where the transistor has maximum value, and both saturation and point size indicate the magnitude of that value.CC BY · PubMed Central
Fig 12 Relating dimensions to known signals to understanding the population code. (A) For each of the recovered dimensions in Fig 11 we compute the correlation in time with 25 known signals inside the process. As we know the purpose of these signals we can measure how well the dimensions explain true underlying function. (B) Dimension 1 is strongly correlated with the processor clock CLK0, whereas (C) dimension 4 is correlated with the 180-degree out of phase CLK1OUT signal. (D) dimension 0 is strongly correlated with signal RW, indicating the processor switching between reading and writing memory.
Fig 12 Relating dimensions to known signals to understanding the population code. (A) For each of the recovered dimensions in Fig 11 we compute the correlation in time with 25 known signals inside the process. As we know the purpose of these signals we can measure how well the dimensions explain true underlying function. (B) Dimension 1 is strongly correlated with the processor clock CLK0, whereas (C) dimension 4 is correlated with the 180-degree out of phase CLK1OUT signal. (D) dimension 0 is strongly correlated with signal RW, indicating the processor switching between reading and writing memory.CC BY · PubMed Central
Fig 13 Understanding the processor. (A) For the processor we understand its hierarchical organization as well as which part of the silicon implements which function. For each of these “functional modules” we know how the outputs depend on the inputs. (B) For the brain, it is harder to be sure. The primate visual system is often depicted in a similar way, such as this diagram adapted from the classic Felleman and vanEssen [66] diagram. These areas are primarially divided according to anatomy, but there is extensive debate about the ideal way of dividing the brain into functional areas. Moreover, we currently have little of an understanding how each area’s outputs depend on its inputs.
Fig 13 Understanding the processor. (A) For the processor we understand its hierarchical organization as well as which part of the silicon implements which function. For each of these “functional modules” we know how the outputs depend on the inputs. (B) For the brain, it is harder to be sure. The primate visual system is often depicted in a similar way, such as this diagram adapted from the classic Felleman and vanEssen [66] diagram. These areas are primarially divided according to anatomy, but there is extensive debate about the ideal way of dividing the brain into functional areas. Moreover, we currently have little of an understanding how each area’s outputs depend on its inputs.CC BY · PubMed Central

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Metadata

Authors
Jonas, E., & Paul Kording, K.
Affiliations
University of California, Berkeley (US)
Corresponding author
Jonas, Eric
ORCID
Venue
PLoS Computational Biology
Publisher
openRxiv
Published
2016-05-26
Volume
13
Issue
1
Pages
e1005268-e1005268
ISSN
1553-734X (ISSN-L)
DOI
10.1101/055624
PMC
PMC5230747
OpenAlex
W2951107471
Cited by
329
References
83
Citations by year
2026: 20 · 2025: 41 · 2024: 29 · 2023: 53 · 2022: 37 · 2021: 31 · 2020: 29 · 2019: 23 · 2018: 33 · 2017: 24 · 2016: 8
FWCI
16.90
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Science Foundation (SN10040 DE-SC0012463, 1139158)
  • U.S. Department of Energy (DESC0012463)
  • Kavli Foundation
  • Cisco Systems
  • Amazon Web Services
  • Thomas and Stacey Siebel Foundation
  • NetApp
  • VMware
  • National Institutes of Health
  • Directorate for Computer and Information Science and Engineering (CCF-1139158)
  • Defense Advanced Research Projects Agency (FA8750, XData Award FA8750-12-2-0331, FA8750-12-2-0331)
  • Samsung
  • National Institute of Mental Health (R01MH103910)
  • National Eye Institute (R01EY021579)
  • National Institute of Neurological Disorders and Stroke (R01NS074044)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
Keywords
microprocessors, multimodal dataset, nonlinear dynamical systems, time series analysis, structure discovery
MeSH terms
Algorithms, Computer Simulation, Microcomputers, Models, Neurological, Neurosciences, Databases, Factual, Neural Networks, Computer, Video Games, Computational Biology, Connectome

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