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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)

Abstract

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.

Keywords

Artificial neural network Spiking neural network Computational neuroscience Conductance models 

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Copyright information

© Springer Nature Switzerland AG 2019

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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