Viewing Rate-Based Neurons as Biophysical Conductance Outputting Models

  • Martinius Knudsen
  • Sverre HendsethEmail author
  • Gunnar TufteEmail author
  • Axel SandvigEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11493)


In the field of computational neuroscience, spiking neural network models are generally preferred over rate-based models due to their ability to model biological dynamics. Within AI, rate-based artificial neural networks have seen success in a wide variety of applications. In simplistic spiking models, information between neurons is transferred through discrete spikes, while rate-based neurons transfer information through continuous firing-rates. Here, we argue that while the spiking neuron model, when viewed in isolation, may be more biophysically accurate than rate-based models, the roles reverse when we also consider information transfer between neurons. In particular we consider the biological importance of continuous synaptic signals. We show how synaptic conductance relates to the common rate-based model, and how this relation elevates these models in terms of their biological soundness. We shall see how this is a logical relation by investigating mechanisms known to be present in biological synapses. We coin the term ‘conductance-outputting neurons’ to differentiate this alternative view from the standard firing-rate perspective. Finally, we discuss how this fresh view of rate-based models can open for further neuro-AI collaboration.


Artificial neural network Spiking neural network Computational neuroscience Conductance models 


  1. 1.
    Aaser, P., et al.: Towards making a cyborg: a closed-loop reservoir-neuro system. In: Proceedings of the 14th European Conference on Artificial Life ECAL 2017, pp. 430–437. MIT Press, Cambridge (2017).
  2. 2.
    Attneave, F., B., M., Hebb, D.O.: The organization of behavior: a neuropsychological theory. Am. J. Psychol. 63(4), 633 (2006). Scholar
  3. 3.
    Brette, R.: Philosophy of the spike: rate-based vs. spike-based theories of the brain. Front. Syst. Neurosci. 9, 151 (2015). Scholar
  4. 4.
    Buchanan, K.A., Mellor, J.: The activity requirements for spike timing-dependent plasticity in the hippocampus. Front. Synaptic Neurosci. 2, 11 (2010). Scholar
  5. 5.
    Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol. Cybern. 95(1), 1–19 (2006). Scholar
  6. 6.
    Clopath, C., Gerstner, W.: Voltage and spike timing interact in STDP - a unified model. Front. Synaptic Neurosci. 2, 25 (2010). Scholar
  7. 7.
    Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015). Scholar
  8. 8.
    Gerstner, W., Kreiter, A.K., Markram, H., Herz, A.V.: Neural codes: firing rates and beyond. Proc. Nat. Acad. Sci. U.S.A. 94(24), 12740–1 (1997). Scholar
  9. 9.
    Honoré, T., Lauridsen, J., Krogsgaard-Larsen, P.: The binding of [3H]AMPA, a structural analogue of glutamic acid, to rat brain membranes. J. Neurochem. (1982). Scholar
  10. 10.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004). Scholar
  11. 11.
    de Kamps, M., van der Velde, F.: From artificial neural networks to spiking neuron populations and back again. Neural Netw. 14(6–7), 941–953 (2001). Scholar
  12. 12.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, vol. 4. McGraw-Hill Education, New York (2013).
  13. 13.
    Kheradpisheh, S.R., Ganjtabesh, M., Masquelier, T.: Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition. Neurocomputing 205, 382–392 (2016). Scholar
  14. 14.
    Kheradpisheh, S.R., Ghodrati, M., Ganjtabesh, M., Masquelier, T.: Deep network scan resemble human feed-forward vision in invariant object recognition. Sci. Rep. 6 (2016).
  15. 15.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997). Scholar
  16. 16.
    Mainen, Z.F., Seinowski, T.J.: Reliability of spike timing in neocortical neurons. Science (1995). Scholar
  17. 17.
    Markram, H., Gerstner, W., Sjøstrøm, P.J.: Spike-timing-dependent plasticity: a comprehensive overview. Front. Res. Topics 4, 2010–2012 (2012). Scholar
  18. 18.
    Medium: Google brain’s co-inventor tells why he’s building Chinese neural networks: Andrew Ng on the state of deep learning at Baidu. Medium (2015)Google Scholar
  19. 19.
    Meldrum, B.S.: Glutamate as a neurotransmitter in the brain: review of physiology and pathology. J. Nutr. 130, 1007S-15S (2000). 10736372CrossRefGoogle Scholar
  20. 20.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (2010).
  21. 21.
  22. 22.
    Rumelhart, D.E., Widrow, B., Lehr, M.A.: The basic ideas in neural networks. Commun. ACM (1994). Scholar
  23. 23.
    Shouval, H.Z., Wang, S.S.H., Wittenberg, G.M.: Spike timing dependent plasticity: a consequence of more fundamental learning rules. Front. Comput. Neurosci. 4, 1–13 (2010). Scholar
  24. 24.
    Sompolinsky, H.: Computational neuroscience: beyond the local circuit. Current Opinion Neurobiol. 25, xiii–xviii (2014). Scholar
  25. 25.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3(9), 919–926 (2000). Scholar
  26. 26.
    Sterratt, D., Graham, B., Gillies, A., Willshaw, D.: Principles of Computational Modelling in Neuroscience. Cambridge University Press (2011). Scholar
  27. 27.
    Wolfram, S.: Cellular automata as models of complexity. Nature 311(5985), 419–424 (1984). Scholar
  28. 28.
    Wurtz, R.H.: Visual receptive fields of striate cortex neurons in awake monkeys. J. Neurophysiol. (1969). Scholar

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Authors and Affiliations

  1. 1.Department of Engineering CyberneticsNTNUTrondheimNorway
  2. 2.Department of Computer ScienceNTNUTrondheimNorway
  3. 3.Department of Neuromedicine and Movement ScienceNTNUTrondheimNorway

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