Functional Tests Guide Complex “Fidelity“ Tradeoffs in Whole-Brain Emulation

Authors

  • Charl Linssen Carboncopies Foundation for Substrate-Independent Minds, 2443 Fillmore St. #380-6190, San Francisco, CA 94115, USA
  • Randal Koene Carboncopies Foundation for Substrate-Independent Minds, 2443 Fillmore St. #380-6190, San Francisco, CA 94115, USA

DOI:

https://doi.org/10.55613/jeet.v35i1.152

Keywords:

brain model, computational neuroscience, whole-brain emulation (WBE), substrate-independent mind (SIM), mind uploading

Abstract

The human brain can be understood as a vast network of neurons connected via synapses, the state of which is characterized by ion concentrations, phosphorylation patterns, receptor densities, etc. It is plausible that a mechanistic simulation at the scale of the whole brain (a “whole-brain emulation“ or WBE) will be made, raising questions about moral status and personal agency. Creating a dynamical model of the brain presents a complex tradeoff between better performance, and data collection and operating costs. To make informed scientific, engineering as well as personal decisions, a set of tests should be defined, that quantify the performance of the individual on a comprehensive repertoire of skills in a variety of domains. "Fidelity" can then be defined as a measure of how well the behavior of the model corresponds with the behavior of the original individual, or with respect to stereotyped brains. Models can subsequently be optimized to obtain the highest fidelity. Nevertheless, an overall measure of fidelity is the outcome of a complex, high-dimensional optimization problem (that of choosing the parameters for a WBE) and remains in and of itself (as a measure or index) challenging to define. Different people and organizations are expected to make different tradeoffs based on a diverse set of criteria. Consequently, there can be multiple variants on offer for the translation from an original, biological brain to a WBE. If some variants are deemed cognitively superior, but are available only at a high cost, then this could have undesired socioeconomic effects where only those who are wealthy can afford the higher-tier emulations. However, competition between different WBE providers attempting to achieve the highest fidelity at the lowest cost could help drive overall costs down. A framework of ethical standards pertaining to model fidelity should be defined, which should recommend a minimum set of standardized tests.

References

(Allen 2022) Allen Institute for Brain Science, Allen Institute for Cell Science. 2022. ”Allen Brain Atlas: Data Portal.” https://celltypes.brain-map.org/ (accessed on October 17th, 2022).

(Amsalem et al. 2020) Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod Kumbhar, Felix Schürmann and Idan Segev. 2020. ”An efficient analytical reduction of detailed nonlinear neuron models.” Nat Commun 11, 288

(Bartol et al. 2015) Bartol T.M. Jr., Bromer C., Kinney J., Chirillo J.N., Bourne J.N., Harris K.M. and Sejnowski T.J. 2015. ”Nanoconnectomic upper bound on the variability of synaptic plasticity.” eLife 4:e10778.

(Baselga-Garriga et al. 2022) Baselga-Garriga, C., Rodriguez, P. and Yuste, R. 2022. ”Neuro Rights: A Human Rights Solution to Ethical Issues of Neurotechnologies.” In: López-Silva, P., Valera, L. (eds) ”Protecting the Mind. Ethics of Science and Technology Assessment.” Vol 49. Springer, Cham.

(Bates et al. 2020) Bates A.S., Schlegel P., Roberts R.J.V., Drummond N., Tamimi I.F.M., Turnbull R., Zhao X., Marin E.C., Popovici P.D., Dhawan S., Jamasb A., Javier A., Capdevila L.S.C., Li F., Rubin G.M., Waddell S., Bock D.D., Costa M. and Jefferis G.S.X.E. 2020. ”Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain.” Current Biology, Volume 30(16):3183-3199.e6.

(Bayne et al. 2024) Bayne T., Seth A.K., Massimini M., Shepherd J., Cleeremans A., Fleming S.M., Malach R., Mattingley J.B., Menon D.K., Owen A.M., Peters M.A.K., Razi A. and Mudrik L. 2024. ”Tests for consciousness in humans and beyond.” Trends in Cognitive Sciences

(Bileh et al. 2020) Yazan N. Billeh, Binghuang Cai, Sergey L. Gratiy, Kael Dai, Ramakrishnan Iyer, Nathan W. Gouwens, Reza Abbasi-Asl, Xiaoxuan Jia, Joshua H. Siegle, Shawn R. Olsen, Christof Koch, Stefan Mihalas and Anton Arkhipov. 2020. ”Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex.” Neuron, Volume 106, Issue 3, Pages 388-403.e18

(Cannon et al. 2010) Cannon R.C., O’Donnell C. and Nolan M.F. 2010. ”Stochastic ion channel gating in dendritic neurons: morphology dependence and probabilistic synaptic activation of dendritic spikes.” PLoS Comput Biol 6: e1000886.

(Citri and Malenka 2008) Citri, A. and Malenka, R.C. 2008. ”Synaptic plasticity: multiple forms, functions, and mechanisms.” Neuropsychopharmacology, 33(1), pp.18-41.

(Deary et al. 2021) Deary I.J., Cox S.R. and Hill W.D. 2022. ”Genetic variation, brain, and intelligence differences.” Molecular Psychiatry 27:335–353.

(Dongés et al. 2012) Dongés B., Haupt L.M., Lea R.A., Chan, Shum D.H.K. and Griffiths L.R. 2012. ”Role of the apolipoprotein E and catechol-O-methyltransferase genes in prospective and retrospective memory traits.” Gene, Volume 506, Issue 1: 135-140.

(Dorkenwald et al. 2023) Dorkenwald S., Matsliah A., Sterling A.R., Schlegel P., Yu S.C., McKellar C.E., Lin A., Costa M., Eichler K., Yin Y., Silversmith W., Schneider-Mizell C., Jordan C.S., Brittain D., Halageri A., Kuehner K., Ogedengbe O., Morey R., Gager J., Kruk K., Perlman E., Yang R., Deutsch D., Bland D., Sorek M., Lu R., Macrina T., Lee K., Bae J.A., Mu S., Nehoran B., Mitchell E., Popovych S., Wu J., Jia Z., Castro M., Kemnitz N., Ih D., Bates A.S., Eckstein N., Funke J., Collman F., Bock D.D., Jefferis G.S.X.E., Seung H.S., Murthy M. and FlyWire Consortium. 2023. ”Neuronal wiring diagram of an adult brain.” bioRxiv.

(Eliasmith and Trujillo 2024) C. Eliasmith and O. Trujillo. 2014. ”The use and abuse of large-scale brain models.” Current Opinion in Neurobiology, Volume 25, Pages 1-6.

(Fagella 2020) Faggella, D. 2020. ”Simulation #506 Dan Faggella - Programmatically Generated Everything.” Interview. https://simulationseries.com/ https://youtu.be/lyBGgE6wYR0, accessed 17 October 2022.

(Frankland et al. 2019) Paul W. Frankland, Sheena A. Josselyn and Stefan Köhler. 2019. ”The neurobiological foundation of memory retrieval.” Nature Neuroscience volume 22, pages 1576–1585.

(Fulton and Briggman 2021) Fulton, K. A. and Briggman, K. L. 2021. ”Permeabilization-free en bloc immunohistochemistry for correlative microscopy.” eLife 10, e63392.

(Goriounova and Mansvelder 2019) Goriounova N.A., Mansvelder H.D. 2019. ”Genes, Cells and Brain Areas of Intelligence.” Front. Hum. Neurosci. 13:44.

(Gutzen et al. 2018) Gutzen R., von Papen M., Trensch G., Quaglio P., Grün S. and Denker M. 2018. ”Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data.” Front. Neuroinform., 19.

(Hanuschkin et al. 2010) Hanuschkin A., Kunkel S., Helias M., Morrison A. and Diesmann M. 2010. ”A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations.” Frontiers in Neuroinformatics. Vol 4:113.

(Herz et al. 2006) Herz A.V.M., Gollisch T., Machens C.K. and Jaeger D. 2006. ”Modeling single-neuron dynamics and computations: a balance of detail and abstraction.” Science 314(5796):80–85.

(Holt and Koch 1999) Holt G.R. and Koch C. 1999. ”Electrical interactions via the extracellular potential near cell bodies.” J Comput Neurosci . 1999 Mar-Apr; Vol. 6(2):169-184.

(Horsman et al. 2014) Horsman C., Stepney S., Wagner R.C. and Kendon V. 2014. ”When does a physical system compute?” Proc. R. Soc. A 470: 20140182.

(IEEE 2019) IEEE Computer Society. 2019. ”IEEE Standard for Floating-Point Arithmetic.” 2019-07-22; pp. 1–84.

(Nader et al. 2000) Nader K., Schafe G. and Le Doux J. 2000. ”Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval.” Nature 406: 722–726.

(Koene 2012) Koene R.A. 2012. ”Fundamentals of whole brain emulation: State, transition and update representations.” International Journal of Machine Consciousness, 4(01), 5-21.

(Krzanowski and Krzanowski 2023) R. Krzanowski and J. Krzanowski. 2023. ”Does whole brain emulation entail the emulation of mental disorders?” Argument Biannual Philosophical Journal 13(1):29-42.

(Müller and Indiveri 2015) L.K. Müller and G. Indiveri. 2015. ”Rounding Methods for Neural Networks with Low Resolution Synaptic Weights.” arXiv:1504.05767v1.

(Pearce 2023) Pearce, D. (wireheading.com). 2023. ”Wirehead Hedonism versus paradise engineering.” Retrieved Aug 8th, 2023.

(Petrovici et al. 2014) Mihai A. Petrovici, Bernhard Vogginger, Paul Müller, Oliver Breitwieser, Mikael Lundqvist, Lyle Muller, Matthias Ehrlich, Alain Destexhe, Anders Lansner, René Schüffny, Johannes Schemmel and Karlheinz Meier. 2014. ”Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms.” PLoS ONE 9(10): e108590.

(Pfeil et al. 2012) T. Pfeil, T.C. Potjans, S. Schrader, W. Potjans, J. Schemmel, M. Diesmann and K. Meier. 2012. ”Is a 4-bit synaptic weight resolution enough? – constraints on enabling spike-timing dependent plasticity in neuromorphic hardware.” Front. Neurosci. Sec. Neuromorphic Engineering Volume 6.

(Rosenblueth and Wiener 1945) Arturo Rosenblueth and Norbert Wiener. 1945. ”The Role of Models in Science.” Philosophy of Science Vol. 12, No. 4 (Oct., 1945), pp. 316-321. The University of Chicago Press.

(Rothblatt 2013) M. Rothblatt. 2013. ”Techno-immortalism 101: What are mindfiles?” In ”Longevitize! Essays on the science, philosophy & politics of longevity”, ed. F. Cortese, 62–66. Center for Transhumanity.

(Sandberg, 2014) Sandberg, A. 2014. ”Ethics of brain emulations.” Journal of Experimental & Theoretical Artificial Intelligence, 26(3), 439–457.

(Sandberg and Bostrom 2008) Anders Sandberg and Nick Bostrom. 2008. ”Whole Brain Emulation: A Roadmap.” Technical Report #2008-3

(Sorokina et al. 2021) Oksana Sorokina, Colin Mclean, Mike D. R. Croning, Katharina F. Heil, Emilia Wysocka, Xin He, David Sterratt, Seth G. N. Grant, T. Ian Simpson and J. Douglas Armstrong. 2021. ”A unified resource and configurable model of the synapse proteome and its role in disease.” Nature Scientific Reports 11:9967

(Wang et al. 2019) Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. ”GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.” Conference paper at ICLR 2019.

(Winding et al. 2023) Michael Winding, Benjamin D. Pedigo, Christopher L. Barnes, Heather G. Patsolic, Youngser Park, Tom Kazimiers, Akira Fushiki, Ingrid V. Andrade, Avinash Khandelwal, Javier Valdes-Aleman, Feng Li, Nadine Randel, Elizabeth Barsotti, Ana Correia, Richard D. Fetter, Volker Hartenstein, Carey E. Priebe, Joshua T. Vogelstein, Albert Cardona and Marta Zlatic. 2023. ”The connectome of an insect brain.” Science vol. 379, no. 6636

(Wybo et al. 2021) Willem AM Wybo, Jakob Jordan, Benjamin Ellenberger, Ulisses Marti Mengual, Thomas Nevian and Walter Senn. 2021. ”Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses.” eLife 10:e60936.

(Xu et al. 2020) Xu, C.S., Pang, S., Hayworth, K.J., Hess, H.F. 2020. ”Transforming FIB-SEM Systems for Large-Volume Connectomics and Cell Biology.” In: Wacker, I., Hummel, E., Burgold, S., Schröder, R. (eds) ”Volume Microscopy, Neuromethods”. Vol. 155. Humana, New York, NY.

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Published

2025-03-17

How to Cite

Functional Tests Guide Complex “Fidelity“ Tradeoffs in Whole-Brain Emulation. (2025). Journal of Ethics and Emerging Technologies, 35(1), 1-14. https://doi.org/10.55613/jeet.v35i1.152

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