Minds and Machines

, Volume 28, Issue 1, pp 77–91 | Cite as

Toward Analog Neural Computation

  • Corey J. Maley


Computationalism about the brain is the view that the brain literally performs computations. For the view to be interesting, we need an account of computation. The most well-developed account of computation is Turing Machine computation, the account provided by theoretical computer science which provides the basis for contemporary digital computers. Some have thought that, given the seemingly-close analogy between the all-or-nothing nature of neural spikes in brains and the binary nature of digital logic, neural computation could be a species of digital computation. A few recent authors have offered arguments against this idea; here, I review recent findings in neuroscience that further cement the implausibility of this view. However, I argue that we can retain the view that the brain is a computer if we expand what we mean by “computation” to include analog computation. I articulate an account of analog computation as the manipulation of analog representations based on previous work on the difference between analog and non-analog representations, extending a view originally articulated in Shagrir (Stud Hist Philos Sci 41(3):271–279, 2010). Given that analog computation constitutes a significant chapter in the history of computation, this revision of computationalism to include analog computation is not an ad hoc addition. Brains may well be computers, but of the analog kind, rather than the digital kind.


Computation Analog and digital Neural signaling 


  1. Alle, H., & Geiger, J. R. P. (2006). Combined analog and action potential coding in hippocampal mossy fibers. Science, 311, 1290–1293.CrossRefGoogle Scholar
  2. Bialowas, A., Rama, S., Zbili, M., Marra, V., Fronzaroli Molinieres, L., Ankri, N., et al. (2015). Analog modulation of spike-evoked transmission in CA3 circuits is determined by axonal Kv1.1 channels in a time-dependent manner. European Journal of Neuroscience, 41(3), 293–304.CrossRefGoogle Scholar
  3. Brody, C., Romo, R., & Kepecs, A. (2003). Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Current Opinion in Neurobiology, 13, 204–211.CrossRefGoogle Scholar
  4. Bromley, A. G. (1990). Analog computing devices. In W. Aspray (Ed.), Computing before computers. Ames, IA: Iowa State University Press.Google Scholar
  5. Chalmers, D. J. (1996). Does a rock implement every finite-state automaton? Synthese, 108(3), 309–333.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Christie, J. M., Chiu, D. N., & Jahr, C. E. (2010). Ca\(^{2+}\)-dependent enhancement of release by subthreshold somatic depolarization. Nature Neuroscience, 14(1), 62–68.CrossRefGoogle Scholar
  7. Dayan, P., & Abbott, L. F. (2005). Theoretical neuroscience. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  8. Debanne, D., Bialowas, A., & Rama, S. (2013). What are the mechanisms for analogue and digital signalling in the brain? Nature Reviews Neuroscience, 14(1), 63–69.CrossRefGoogle Scholar
  9. Egan, F. (1995). Computation and content. Philosophical Review, 104(2), 181–203.CrossRefGoogle Scholar
  10. Egan, F. (2010). Computational models: A modest role for content. Studies in History and Philosophy of Science Part A, 41(3), 253–259.CrossRefGoogle Scholar
  11. Fodor, J. A. (1975). The language of thought. Cambridge, MA: Harvard University Press.Google Scholar
  12. Gallistel, C. R., & King, A. P. (2009). Memory and the computational brain. Malden, MA: Wiley-Blackwell.CrossRefGoogle Scholar
  13. Gerstner, W., Kempter, R., van Hemmen, J. L., & Wagner, H. (1996). A neuronal learning rule for sub-millisecond temporal coding. Nature, 383(6595), 76–81.CrossRefGoogle Scholar
  14. Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  15. Hormuzdi, S. G., Filippov, M. A., Mitropoulou, G., Monyer, H., & Bruzzone, R. (2004). Electrical synapses: A dynamic signaling system that shapes the activity of neuronal networks. Biochimica et Biophysica Acta (BBA)-Biomembranes, 1662(1–2), 113–137.CrossRefGoogle Scholar
  16. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (2012). Principles of neural science (5th ed.). New York, NY: McGraw-Hill Education.Google Scholar
  17. Kole, M. H. P., Letzkus, J. J., & Stuart, G. J. (2007). Axon initial segment Kv1 channels control axonal action potential waveform and synaptic efficacy. Neuron, 55(4), 633–647.CrossRefGoogle Scholar
  18. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.MathSciNetCrossRefGoogle Scholar
  19. Maley, C. J. (2011). Analog and digital, continuous and discrete. Philosophical Studies, 155(1), 117–131.CrossRefGoogle Scholar
  20. Miłkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.Google Scholar
  21. Mindell, D. A. (2002). Between human and machine. Baltimore, MD: Johns Hopkins University Press.Google Scholar
  22. Piccinini, G. (2007). Computational modelling vs. computational explanation: Is everything a Turing machine, and does it matter to the philosophy of mind? Australasian Journal of Philosophy, 85(1), 93–115.MathSciNetCrossRefGoogle Scholar
  23. Piccinini, G. (2008). Computation without representation. Philosophical Studies, 137(2), 205–241.MathSciNetCrossRefGoogle Scholar
  24. Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 34, 453–488.CrossRefGoogle Scholar
  25. Putnam, H. (1988). Representation and reality. Cambridge, MA: MIT Press.Google Scholar
  26. Rama, S., Zbili, M., & Debanne, D. (2015). Modulation of spike-evoked synaptic transmission: The role of presynaptic calcium and potassium channels. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 1853(9), 1933–1939.CrossRefGoogle Scholar
  27. Robertson, J. S. (1964). Analog computation: Definition and characteristics. Annals of the New York Academy of Sciences, 115(1), 553–557.CrossRefGoogle Scholar
  28. Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424.CrossRefGoogle Scholar
  29. Shagrir, O. (2010). Brains as analog-model computers. Studies in History and Philosophy of Science, 41(3), 271–279.CrossRefGoogle Scholar
  30. Söhl, G., Maxeiner, S., & Willecke, K. (2005). Expression and functions of neuronal gap junctions. Nature Reviews Neuroscience, 6(3), 191–200.CrossRefGoogle Scholar
  31. Sullivan, D. W., & Levy, W. B. (2003). Quantal synaptic failures improve performance in a sequence learning model of hippocampal CA3. Neurocomputing, 52–54, 397–401.CrossRefGoogle Scholar
  32. Ulmann, B. (2013). Analog computing. Berlin: De Gruyter.CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.University of KansasLawrenceUSA

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