<p>As seen in "Neural Engineering" by Eliasmith et al. Chapter 4, complicated neuron models have greater computational abilities and match neural data more realistically.</p>

<h1>Computational Capabilities</h1>

<p>As seen in the following table (taken from Neural Engineering) Adaptive LIF neurons, by virtue of their temporally varying firing patterns (i.e. a constant input will cause different spike patterns over time) encode more information per spike:</p>

<p><img src="https://i.sstatic.net/vSe5I.png" alt="Neural Engineering Table"></p>

<p>As seen in this table, there's a trade-off between the complexity of the information represented by the neuron and it's computational cost. However, this cost is calculated on traditional Von-Neumann CPUs. It is possible to create neuromorphic hardware to minimize this cost while maximizing computational ability. For more information, see "<a href="http://compneuro.uwaterloo.ca/publications/stoeckel2018.html" rel="nofollow noreferrer">Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework</a>" which shows how non-linear dendrites can allow for powerful forms of computation with minimal additional neuromorphic hardware cost.</p>

<h1>Matching Biological Data</h1>

<p>The aforementioned temporal variance of the Adaptive LIF is also used to matching neural data as seen in <a href="http://www.jneurosci.org/content/26/14/3667.full" rel="nofollow noreferrer">this model of working memory</a>, which would have not been possible with regular LIF neurons. Using even more complicated models can allow for matching the effects of drugs, as shown in "<a href="http://compneuro.uwaterloo.ca/publications/duggins2017b.html" rel="nofollow noreferrer">Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory</a>".</p>

CC BY-SA 4.0
2015-01-22T20:33:39.707Z
2018-07-06T02:57:51.220Z
2018-07-06T02:57:51.220Z
Seanny123
<p>The NEF allows you to use almost any neuron model as long as it has an equation for it's activity and it's spikes in some way. Usually, a simple leaky-integrate-fire (LIF) neuron model is used, but even in the software Nengo there are various neurons available for modelling, such as adaptive LIF neurons and Poisson neurons. What are the benefits of using these more complex neurons and how have these benefits been leveraged in the past?</p>

<neuroscience><theoretical-neuroscience><nef>
Benefits of using more complicated neuron models in NEF models
Seanny123
# Benefits of using more complicated neuron models in NEF models

(Asked by: username_0 on 2015-01-22)

The NEF allows you to use almost any neuron model as long as it has an equation for it's activity and it's spikes in some way. Usually, a simple leaky-integrate-fire (LIF) neuron model is used, but even in the software Nengo there are various neurons available for modelling, such as adaptive LIF neurons and Poisson neurons. What are the benefits of using these more complex neurons and how have these benefits been leveraged in the past?

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


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## 1 answers

### Answer by username_0 on 2015-01-22. Score: 2 | Accepted answer

As seen in "Neural Engineering" by Eliasmith et al. Chapter 4, complicated neuron models have greater computational abilities and match neural data more realistically.

# Computational Capabilities

As seen in the following table (taken from Neural Engineering) Adaptive LIF neurons, by virtue of their temporally varying firing patterns (i.e. a constant input will cause different spike patterns over time) encode more information per spike:

![Neural Engineering Table](https://i.sstatic.net/vSe5I.png)

As seen in this table, there's a trade-off between the complexity of the information represented by the neuron and it's computational cost. However, this cost is calculated on traditional Von-Neumann CPUs. It is possible to create neuromorphic hardware to minimize this cost while maximizing computational ability. For more information, see "[Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework](http://compneuro.uwaterloo.ca/publications/stoeckel2018.html)" which shows how non-linear dendrites can allow for powerful forms of computation with minimal additional neuromorphic hardware cost.

# Matching Biological Data

The aforementioned temporal variance of the Adaptive LIF is also used to matching neural data as seen in [this model of working memory](http://www.jneurosci.org/content/26/14/3667.full), which would have not been possible with regular LIF neurons. Using even more complicated models can allow for matching the effects of drugs, as shown in "[Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory](http://compneuro.uwaterloo.ca/publications/duggins2017b.html)".


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