<p>First, let's take a look at <a href="http://compneuro.uwaterloo.ca/files/publications/stewart.2012d.pdf" rel="nofollow noreferrer">the basic principles of NEF</a>.</p>

<p>The first two principles (Representation and Computation) do seem analogous to trained ANN models. Additionally, with the hPES learning rule that I've described <a href="https://cogsci.stackexchange.com/questions/7782/how-does-hpes-compare-to-the-learning-rates-of-anns">here</a>, they seem to have the same learning (gradient-descent) capabilities.</p>

<p>Where the NEF differentiates itself the most from ANNs is when it starts to describe dynamics (oscillators, attractors). For a description on how it does this and what it accomplishes, check out Terry Stewart's course notes found <a href="http://nbviewer.ipython.org/github/tcstewar/syde556/blob/master/SYDE%20556%20Lecture%205%20Dynamics.ipynb" rel="nofollow noreferrer">here</a>.</p>

<p>Describing dynamics is important in the brain, since simple oscillators and integrators seem to be the foundation of many different cognitive abilities such as working memory and motor control.</p>

CC BY-SA 3.0
2014-09-12T15:51:35.547Z
What the NEF does is translate neural coding into the solving of differential equaions (well functions more generally)  You might argue that it's a specific implementation of ANN.
Keegan Keplinger
CC BY-SA 3.0
2014-10-15T18:46:24.193Z
@KeeganKeplinger When you say "specific implementation" of ANNs, it could be interpreted as saying that they both have the same functional capabilities, but I'm not sure I understand your evidence for this. Could you help me out?
Seanny123
CC BY-SA 3.0
2014-10-15T19:03:18.617Z
an ANN is just a network of simple neurons (usually designed in some connective pattern, designed to serve a purpose).  NEF utilizes an ANN to solve differential equations.

So of course, ANN, being the more general term, has more capabilities... but once you choose an instance of an ANN for a specific purpose, you begin to limit the capabilities of the ANN towards your purpose.
Keegan Keplinger
CC BY-SA 3.0
2014-10-15T19:17:11.360Z
So when you say ANN, you don't mean specifically the back-prop type. You just mean any system that has nodes that are neurons that are then arranged in a manner to imitate neural computation?
Seanny123
CC BY-SA 3.0
2014-10-15T19:28:34.373Z
When I see ANN I think of individual neurons modeled for simplicity and problem solving, not physiology (so not ion current models based on actual channel kinetics).  Usually the goal is to compute something, not understand neurons - I think that the core definition of ANN is in the goal of the network design, not the type of neurons used.  The two I mostly see in such approaches are integrate-and-fire and firing rate models.  Though I'm no authority here, my research in ion-conductance models.  I know sometimes you can deduce group neuron behavior using ANN's, so there's likely a fuzzy line
Keegan Keplinger
CC BY-SA 4.0
2014-09-05T21:02:02.357Z
2018-07-06T02:38:30.767Z
2018-07-06T02:38:30.767Z
Seanny123
<p>Aside from obvious biological plausibility, from a computational standpoint, what's the motivation of using the Neural Engineering Framework (NEF) instead of Artificial Neural Networks (ANNs) for computing functions? From what I can tell, they both approximate functions, have the ability to learn new functions and the ability to create self-organizing networks. What makes the NEF different?</p>

<theoretical-neuroscience><neural-network><nef>
What's the functional difference between the NEF and normal ANNs?
Seanny123
# What's the functional difference between the NEF and normal ANNs?

(Asked by: username_0 on 2014-09-05)

Aside from obvious biological plausibility, from a computational standpoint, what's the motivation of using the Neural Engineering Framework (NEF) instead of Artificial Neural Networks (ANNs) for computing functions? From what I can tell, they both approximate functions, have the ability to learn new functions and the ability to create self-organizing networks. What makes the NEF different?

**Tags:** theoretical-neuroscience, neural-network, nef


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## 1 answers

### Answer by username_0 on 2014-09-05. Score: 5 | Accepted answer

First, let's take a look at [the basic principles of NEF](http://compneuro.uwaterloo.ca/files/publications/stewart.2012d.pdf).

The first two principles (Representation and Computation) do seem analogous to trained ANN models. Additionally, with the hPES learning rule that I've described [here](https://cogsci.stackexchange.com/questions/7782/how-does-hpes-compare-to-the-learning-rates-of-anns), they seem to have the same learning (gradient-descent) capabilities.

Where the NEF differentiates itself the most from ANNs is when it starts to describe dynamics (oscillators, attractors). For a description on how it does this and what it accomplishes, check out Terry Stewart's course notes found [here](http://nbviewer.ipython.org/github/tcstewar/syde556/blob/master/SYDE%20556%20Lecture%205%20Dynamics.ipynb).

Describing dynamics is important in the brain, since simple oscillators and integrators seem to be the foundation of many different cognitive abilities such as working memory and motor control.

> **Comments:**
> **username_1**: What the NEF does is translate neural coding into the solving of differential equaions (well functions more generally)  You might argue that it's a specific implementation of ANN.  
> **username_0**: @KeeganKeplinger When you say "specific implementation" of ANNs, it could be interpreted as saying that they both have the same functional capabilities, but I'm not sure I understand your evidence for this. Could you help me out?  
> **username_1**: an ANN is just a network of simple neurons (usually designed in some connective pattern, designed to serve a purpose).  NEF utilizes an ANN to solve differential equations.  So of course, ANN, being the more general term, has more capabilities... but once you choose an instance of an ANN for a specific purpose, you begin to limit the capabilities of the ANN towards your purpose.  
> **username_0**: So when you say ANN, you don't mean specifically the back-prop type. You just mean any system that has nodes that are neurons that are then arranged in a manner to imitate neural computation?  
> **username_1**: When I see ANN I think of individual neurons modeled for simplicity and problem solving, not physiology (so not ion current models based on actual channel kinetics).  Usually the goal is to compute something, not understand neurons - I think that the core definition of ANN is in the goal of the network design, not the type of neurons used.  The two I mostly see in such approaches are integrate-and-fire and firing rate models.  Though I'm no authority here, my research in ion-conductance models.  I know sometimes you can deduce group neuron behavior using ANN's, so there's likely a fuzzy line  

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