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The primary learning mechanism of artificial neural networks (ANN) is back-propagation, which is not biologically plausible.

Trevor Berkolay created an alternative to this learning with the Neurological Engineering Framework (NEF) and Nengo called hPES (Homeostatic Prescribed Error Sensitivity) 1. But how does it's learning capabilities compare to the standard supervised and unsupervised learning of ANNs in terms of computational power required and speed of learning?

1 See also, "How to Build a Brain" by Chris Eliasmith chapter 6.4

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According to the paper, the advantage of this new approach over conventional ANNs, Deep Belief Networks (DBN) and Self-Organising Networks (SON) are:

  1. Remains functional during online learning.
  2. Requires only two layers connected with simultaneous supervised and unsupervised learning
  3. Employs spiking neuron models to reproduce central features of biological learning, such as spike-timing dependent plasticity (STDP)

So arguably, hPES is superior to ANNs in terms of capability, but in terms of performance, you'll have to compare the code from his experiments with ANNs meant to accomplish the same task, but since both methodologies are designed with different purposes in mind, it might not be worth comparing.

Finally, note that although hPES is more biologically plausible, it still has some of the same problems that ANNs have. Namely, the parameters that it takes in must be optimized for the specific task it's learning (some approaches for solving this with ANNs include Genetic Algorithms). The author mentions this as being part of future work.

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