Skip to main content
SpringerLink
Log in

Why is There No Successful Whole Brain Simulation (Yet)?

  • Original Article
  • Published:
Biological Theory Aims and scope Submit manuscript

Abstract

With the advent of powerful parallel computers, efforts have commenced to simulate complete mammalian brains. However, so far none of these efforts has produced outcomes close to explaining even the behavioral complexities of animals. In this article, we suggest four challenges that ground this shortcoming. First, we discuss the connection between hypothesis testing and simulations. Typically, efforts to simulate complete mammalian brains lack a clear hypothesis. Second, we treat complications related to a lack of parameter constraints for large-scale simulations. To demonstrate the severity of this issue, we review work on two small-scale neural systems, the crustacean stomatogastric ganglion and the Caenorhabditis elegans nervous system. Both of these small nervous systems are very thoroughly, but not completely understood, mainly due to issues with variable and plastic parameters. Third, we discuss the hierarchical structure of neural systems as a principled obstacle to whole-brain simulations. Different organizational levels imply qualitative differences not only in structure, but in choice and appropriateness of investigative technique and perspective. The challenge of reconciling different levels also undergirds the challenge of simulating and hypothesis testing, as modeling a system is not the same thing as simulating it. Fourth, we point out that animal brains are information processing systems tailored very specifically for the ecological niches the respective animals live in.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. We understand the hierarchical organization of the nervous system as one mediated primarily by part-whole relations, where what is considered a whole at one level (say, a cell) is a part at another level (say, the tissue level). This is a distinction from other conceptions of hierarchical organization, such as van Essen and Maunsell’s (1983) hierarchy of functional streams in the visual cortex, and from David Marr’s (1982) trilevel distribution of the algorithmic, computational, and implementational levels. The chief difference here is that these conceptions of level focus on the transmission of information between functional units, rather than track compositional relations between units of nature.

  2. One important issue that appears here concerns when abstraction (removing detail until a desired grain of description is attained) crosses the line into an idealization (actively distorting factual details of a system). Though both idealization and abstraction play positive, even necessary roles in science, there are distinct issues when one or the other is pursued. Presumably, modelers and simulators of whole brains are interested in abstraction, given their claims of producing “accurate” models of neural systems (see especially the preceding discussion). Nonetheless, we’d like to point out that such aims may pass into the realm of idealization, where a different set of issues crop up (see especially Potochnik 2017). Many thanks to an anonymous reviewer for bringing this to our attention.

References

  • Almog M, Korngreen A (2016) Is realistic neuronal modeling realistic? J Neurophysiol. https://doi.org/10.1152/jn.00360.2016

    Article  Google Scholar 

  • Ananthanarayanan R, Esser SK, Simon HD, Modha DS (2009) The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses. In: Proceedings of the conference on high performance computing networking, storage and analysis, SC09, Portland, 14–20 November 2009, pp 1–12

  • Blau A, Callaly F, Cawley S, Coffey A, De Mauro A et al (2014) The Si elegans project—the challenges and prospects of emulating Caenorhabditis elegans. In: Duff A, Lepora NF, Mura A, Prescott TJ, Verschure PFMJ (eds) Biomimetic and biohybrid systems. Living machines 2014, Lecture Notes in Computer Science, vol 8608. Springer, Cham, pp 436–438

    Google Scholar 

  • Borst A, Egelhaaf E (1989) Principles of visual motion detection. Trends Neurosci 12(8):297–306

    Article  Google Scholar 

  • Breakspear M, Stam CJ (2005) Dynamics of a neural system with a multiscale architecture. Philos Trans R Soc B 360:1051

    Article  Google Scholar 

  • Brooks DS (2014) The role of models in the process of epistemic integration: the case of the Reichardt motion detector. Hist Philos Life Sci 36(1):90–113

    Article  Google Scholar 

  • Bullock TH (1984) Comparative neuroscience holds promise for quiet revolutions. Science 225(4661):473–478

    Article  Google Scholar 

  • Campbell DT (1974) Downward causation in hierarchically organised biological systems. In: Ayala FJ, Dobzhansky T (eds) Studies in the philosophy of biology: reduction and related problems. MacMillan, London, pp 179–186

    Chapter  Google Scholar 

  • Ching J, Beck JL, Porter KA (2006) Bayesian state and parameter estimation of uncertain dynamical systems. Probab Eng Mech 21:81–96

    Article  Google Scholar 

  • Craver C (2007) Explaining the brain. Clarendon Press, Oxford

    Book  Google Scholar 

  • De Ruyter van Steveninck RR, Zaagman WH, Mastebroek HAK (1986) Adaptation of transient responses of a movement-sensitive neuron in the visual system of the blowfly Calliphora erythrocephala. Biol Cybern 54:223–236

    Article  Google Scholar 

  • Egelhaaf M, Borst A (1993) A look into the cockpit of the fly: visual orientation, algorithms, and identified neurons. J Neurosci 13(11):4563–4574

    Article  Google Scholar 

  • Eliasmith C, Stewart TC, Choo X, Bekolay T, DeWolf T et al (2012) A large-scale model of the functioning brain. Science 338(6111):1202–1205

    Article  Google Scholar 

  • Eronen MI, Brooks DS (2018) Levels of organization in biology. In: Zalta EN (ed) The Stanford encyclopedia of philosophy. Stanford University, Stanford. https://plato.stanford.edu/archives/spr2018/entries/levels-org-biology/

  • Faumont S, Rondeau G, Thiele TR, Lawton KJ, McCormick KE et al (2011) An image-free opto-mechanical system for creating virtual environments and imaging neuronal activity in freely moving Caenorhabditis elegans. PLoS ONE 6:e24666

    Article  Google Scholar 

  • Gerstein MB, Lu ZJ, Van Nostrand EL, Cheng C, Arshinoff BI et al (2010) Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330:1775–1787

    Article  Google Scholar 

  • Grim P, Rosenberger R, Rosenfeld A, Anderson B, Eason RE (2013) How simulations fail. Synthese 190(12):2367–2390

    Article  Google Scholar 

  • Guttman BS (1976) Commentary: is “levels of organization” a useful biological concept? BioScience 26:112–113

    Article  Google Scholar 

  • Hamood AW, Haddad SA, Otopalik AG, Rosenbaum P, Marder E (2015) Quantitative reevaluation of the effects of short- and long-term removal of descending modulatory inputs on the pyloric rhythm of the crab, Cancer borealis. eNeuro. https://doi.org/10.1523/ENEURO.0058-14.2015

    Article  Google Scholar 

  • Hartmann S (1996) The world as a process. Simulations in the natural and social sciences. In: Hegselmann R, Mueller U, Troitzsch KG (eds) Modelling and simulation in the social sciences from the philosophy of science point of view. Springer, Dordrecht, pp 77–100

    Chapter  Google Scholar 

  • Herz AVM, Gollisch T, Machens CK, Jaeger D (2006) Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314:80–85

    Article  Google Scholar 

  • Hoel EP, Albantakis L, Marshall W, Tononi G (2016) Can the macro beat the micro? Integrated information across spatiotemporal scales. Neurosci Conscious 1:niw012. https://doi.org/10.1093/nc/niw012

    Article  Google Scholar 

  • Honey CJ, Kötter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS 104:10240–10245

    Article  Google Scholar 

  • Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195:215–243

    Article  Google Scholar 

  • Izhikevich EM (2010) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge

    Google Scholar 

  • Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. PNAS 105:3593–3598

    Article  Google Scholar 

  • Krohs U (2008) How digital computer simulations explain real-world processes. Int Stud Philos Sci 22(3):277–292

    Article  Google Scholar 

  • Levins R (1966) The strategy of model building in population biology. Am Sci 54(4):421–431

    Google Scholar 

  • Machado P, Wade J, McGinnity TM (2014) Si elegans: FPGA hardware emulation of C. elegans nematode nervous system. In: 2014 sixth world congress on nature and biologically inspired computing (NaBIC 2014), pp 65–71

  • Marder E, Haddad SA, Goeritz ML, Rosenbaum P, Kispersky T (2015) How can motor systems retain performance over a wide temperature range? Lessons from the crustacean stomatogastric nervous system. J Comp Physiol A 201:851–856

    Article  Google Scholar 

  • Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160

    Article  Google Scholar 

  • Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M et al (2015) Reconstruction and simulation of neocortical microcircuitry. Cell 163:456–492

    Article  Google Scholar 

  • Marr D (1982) Vision: a computational approach. Freeman, San Francisco

    Google Scholar 

  • Meister P, Towbin BD, Pike BL, Ponti A, Gasser SM (2010) The spatial dynamics of tissue-specific promoters during C. elegans development. Genes Dev 24:766–782

    Article  Google Scholar 

  • Merolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, Sawada J et al (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345:668–673

    Article  Google Scholar 

  • Ollivier FJ, Samuelson DA, Brooks DE, Lewis PA, Kallberg ME, Komáromy AM (2004) Comparative morphology of the tapetum lucidum (among selected species). Vet Ophthalmol 7:11–22

    Article  Google Scholar 

  • Palyanov A, Khayrulin S, Larson SD, Dibert A (2012) Towards a virtual C. elegans: a framework for simulation and visualization of the neuromuscular system in a 3D physical environment. In Silico Biol 11:137–147

    Google Scholar 

  • Parker WS (2009) Does matter really matter? Computer simulations, experiments, and materiality. Synthese 169:483–496

    Article  Google Scholar 

  • Potochnik A (2017) Idealization and the aims of science. University of Chicago Press, Chicago

    Book  Google Scholar 

  • Robinson PA, Rennie CJ, Rowe DL, O’Connor SC, Gordon E (2005) Multiscale brain modelling. Philos Trans R Soc Lond B 360:1043–1050

    Article  Google Scholar 

  • Schrödel T, Prevedel R, Aumayr K, Zimmer M, Vaziri A (2013) Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light. Nat Methods 10:1013–1020

    Article  Google Scholar 

  • Schulz DJ, Goaillard JM, Marder E (2006) Variable channel expression in identified single and electrically coupled neurons in different animals. Nat Neurosci 9(3):356

    Article  Google Scholar 

  • Sejnowski TJ, Churchland PS (1994) The computational brain. A Bradford Book, MIT Press, Cambridge

    Google Scholar 

  • Selverston A (2008) Stomatogastric ganglion. Scholarpedia 3:1661

    Article  Google Scholar 

  • Stephan A (1999) Varieties of emergence. Evol Cogn 5(1):49–59

    Google Scholar 

  • Szigeti B, Gleeson P, Vella M, Khayrulin S, Palyanov A et al (2014) OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Front Comput Neurosci. https://doi.org/10.3389/fncom.2014.00137

    Article  Google Scholar 

  • Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH (2009) Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface 6:187–202

    Article  Google Scholar 

  • Torben-Nielsen B, Stiefel KM (2009) Multiscale modeling of cortical neural networks. AIP Conf Proc 1167(1):15–25

    Article  Google Scholar 

  • van der Merwe R, Wan EA (2001) The square-root unscented Kalman filter for state and parameter-estimation. In: 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat No. 01CH37221), vol 6, pp 3461–3464

  • Van Essen DC, Maunsell JH (1983) Hierarchical organization and functional streams in the visual cortex. Trends Neurosci 6:370–375

    Article  Google Scholar 

  • Varier S, Kaiser M (2011) Neural development features: spatio-temporal development of the Caenorhabditis elegans neuronal network. PLoS Comput Biol 7:e1001044

    Article  Google Scholar 

  • Wimsatt W (2007) Re-engineering philosophy for limited beings. Harvard University Press, Cambridge

    Google Scholar 

  • Winsberg E (1999) Sanctioning models: the epistemology of simulation. Sci Context 12(2):275–292

    Article  Google Scholar 

  • Winsberg E (2001) Simulations, models, and theories: complex physical systems and their representations. Philos Sci 68:442–454

    Article  Google Scholar 

  • Winsberg E (2003) Simulated experiments: methodology for a virtual world. Philos Sci 70:105–125

    Article  Google Scholar 

Download references

Acknowledgments

We thank the reviewers of the initial version of this manuscript, our colleagues at the Konrad Lorenz Institute, Drs. Greg Cohen, Jonathan Tapson, and the participants of the 2014 Telluride Neuromorphic Cognition Engineering Workshop for helpful discussion.

Author information

Authors and Affiliations

  1. Neurolinx Research Institute, La Jolla, CA, USA

    Klaus M. Stiefel

  2. Department of Philosophy, University of Cincinnati, Cincinnati, OH, USA

    Daniel S. Brooks

  3. Marine Science Institute, University of the Philippines, Dilliman, Quezon City, Philippines

    Klaus M. Stiefel

Authors
  1. Klaus M. Stiefel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel S. Brooks
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klaus M. Stiefel.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stiefel, K.M., Brooks, D.S. Why is There No Successful Whole Brain Simulation (Yet)?. Biol Theory 14, 122–130 (2019). https://doi.org/10.1007/s13752-019-00319-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13752-019-00319-5

Keywords

Search

Navigation