Biological Cybernetics

, Volume 113, Issue 1–2, pp 179–190 | Cite as

Spiking networks as efficient distributed controllers

  • Fuqiang HuangEmail author
  • ShiNung Ching
Original Article


In the brain, networks of neurons produce activity that is decoded into perceptions and actions. How the dynamics of neural networks support this decoding is a major scientific question. That is, while we understand the basic mechanisms by which neurons produce activity in the form of spikes, whether these dynamics reflect an overlying functional objective is not understood. In this paper, we examine neuronal dynamics from a first-principles control-theoretic viewpoint. Specifically, we postulate an objective wherein neuronal spiking activity is decoded into a control signal that subsequently drives a linear system. Then, using a recently proposed principle from theoretical neuroscience, we optimize the production of spikes so that the linear system in question achieves reference tracking. It turns out that such optimization leads to a recurrent network architecture wherein each neuron possess integrative dynamics. The network amounts to an efficient, distributed event-based controller where each neuron (node) produces a spike if doing so improves tracking performance. Moreover, the dynamics provide inherent robustness properties, so that if some neurons fail, others will compensate by increasing their activity so that the tracking objective is met.


Spiking networks Neural networks Decoding Event-based control 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Department of Electrical and Systems EngineeringWashington UniversitySt. LouisUSA

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