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

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

<p>So arguably, hPES is superior to ANNs in terms of capability, but in terms of performance, you'll have to compare <a href="http://github.com/tbekolay/cogsci2013" rel="nofollow">the code from his experiments</a> 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. Additionally, it should be noted that although the author claims to have created a SON, the validation of this claim in the paper (and his master's thesis) is pretty weak. In the paper he only proves that the SON increases sparsity, which is not what SONs (for example Kohonen networks) are generally used for.</p>

<p>Finally, note that although hPES is <em>more</em> 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) even if it is <em>less</em> sensitive to parameter modification. The author mentions this investigation of parameters as being part of future work.</p>

CC BY-SA 3.0
2015-01-29T23:01:50.347Z
Doesn't hPES only deal with rate codes, and not spike times? A better question would be how does hPES compare to STDP.
Matt Way
CC BY-SA 3.0
2015-01-30T01:59:27.187Z
@MattWay hPES can be used with spiking and rate neurons, like everything in Nengo
Seanny123
CC BY-SA 3.0
2015-01-30T02:11:50.440Z
I didn't say it couldn't.
Matt Way
CC BY-SA 3.0
2015-09-15T23:18:57.263Z
@MattWay reading this again, I'm sorry I mis-understood you. If you consult Trevor Bekolay's Master's thesis you see that hPES captures all aspects of STDP.
Seanny123
CC BY-SA 4.0
2018-06-20T19:28:35.193Z
@MattWay years later, I have a VERY brief summary of how it matches in [this answer](https://psychology.stackexchange.com/a/17400/4397)
Seanny123
CC BY-SA 3.0
2014-07-14T20:35:00.803Z
2015-09-15T23:22:08.330Z
2015-09-15T23:22:08.330Z
Seanny123
<p>The primary learning mechanism of artificial neural networks (ANN) is back-propagation, which is not biologically plausible [2].</p>

<p>Trevor Berkolay created an alternative to this learning with the Neurological Engineering Framework (NEF) and Nengo called <a href="http://mindmodeling.org/cogsci2013/papers/0058/paper0058.pdf" rel="nofollow noreferrer">hPES (Homeostatic Prescribed Error Sensitivity)</a> [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?</p>

<p>[1] See also, "How to Build a Brain" by Chris Eliasmith chapter 6.4</p>

<p>[2] Further details around this claim can be found in the question "<a href="https://psychology.stackexchange.com/q/16269/4397">Is back-prop biologically plausible?</a>"</p>

<p><strong>Note:</strong> This question portrayed ANNs and the NEF as adversarial, which really isn't the case. Spaun the brain model, which is kind of the poster child of the NEF, uses ANNs (Convolutional Neural Networks specifically) converted into spiking neurons in it's vision system.</p>

<cognitive-neuroscience><theoretical-neuroscience><computational-modeling><nef>
How does hPES compare to the learning rates of ANNs?
Seanny123
# How does hPES compare to the learning rates of ANNs?

(Asked by: username_0 on 2014-07-09)

The primary learning mechanism of artificial neural networks (ANN) is back-propagation, which is not biologically plausible [2].

Trevor Berkolay created an alternative to this learning with the Neurological Engineering Framework (NEF) and Nengo called [hPES (Homeostatic Prescribed Error Sensitivity)](http://mindmodeling.org/cogsci2013/papers/0058/paper0058.pdf) [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

[2] Further details around this claim can be found in the question "[Is back-prop biologically plausible?](https://psychology.stackexchange.com/q/16269/4397)"

**Note:** This question portrayed ANNs and the NEF as adversarial, which really isn't the case. Spaun the brain model, which is kind of the poster child of the NEF, uses ANNs (Convolutional Neural Networks specifically) converted into spiking neurons in it's vision system.

**Tags:** cognitive-neuroscience, theoretical-neuroscience, computational-modeling, nef


---

## 1 answers

### Answer by username_0 on 2014-07-14. Score: 3 | Accepted answer

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](http://github.com/tbekolay/cogsci2013) 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. Additionally, it should be noted that although the author claims to have created a SON, the validation of this claim in the paper (and his master's thesis) is pretty weak. In the paper he only proves that the SON increases sparsity, which is not what SONs (for example Kohonen networks) are generally used for.

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) even if it is *less* sensitive to parameter modification. The author mentions this investigation of parameters as being part of future work.

> **Comments:**
> **username_1**: Doesn't hPES only deal with rate codes, and not spike times? A better question would be how does hPES compare to STDP.  
> **username_0**: @MattWay hPES can be used with spiking and rate neurons, like everything in Nengo  
> **username_1**: I didn't say it couldn't.  
> **username_0**: @MattWay reading this again, I'm sorry I mis-understood you. If you consult Trevor Bekolay's Master's thesis you see that hPES captures all aspects of STDP.  
> **username_0**: @MattWay years later, I have a VERY brief summary of how it matches in [this answer](https://psychology.stackexchange.com/a/17400/4397)  

---

