<p>They're mentioning biological plausibility, because they think biological constraints are important for intelligent systems. Also, biologically constrained systems could be easily applied to understanding the brain. Whether biological plausibility is important or not to developing intelligent systems and understanding the brain is a matter of debate.</p>

<p>On one hand, there's the argument that since the brain is the only form of intelligence we know, we should probably imitate it to develop intelligent systems. Also, at the same time, we would learn to understand the brain!</p>

<p>On the other hand, there's the often-repeated argument that we did not get airplanes from imitating birds, but from understanding the principles of aerodynamics. In turn, the principles of aerodynamics were then applied to bird wings and feathers.</p>

<p>For better introduction to this debate applied to understanding the brain and creating scalable intelligence, try reading "<a href="http://onlinelibrary.wiley.com/doi/10.1111/tops.12133/abstract" rel="nofollow noreferrer">Marr's Attacks: On Reductionism and Vagueness</a>" by Eliasmith et al. </p>

CC BY-SA 3.0
2018-01-07T00:44:41.447Z
2018-01-07T00:44:41.447Z
Seanny123
<p>I have found that many authors of machine learning papers, that employ the <em>Hebbian learning</em> rule, refer to the <strong>biological plausibility of it</strong>, as one of the arguments to use it instead of the well known back-propagation algorithm
(<a href="https://pdfs.semanticscholar.org/67d3/93763869e67874f61b4f13b95ae847e55e55.pdf" rel="nofollow noreferrer">Whittington &amp; Bogacz, 2017</a>;<a href="http://eplex.cs.ucf.edu/papers/pugh_alife14.pdf" rel="nofollow noreferrer"> Pugh, Soltoggio, &amp; Stanley, 2014</a>; <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4538293/" rel="nofollow noreferrer">Burms, Caluwaerts, &amp;
Dambre, 2015</a>) (<em>I imagine the same argument is used for other biologically plausible algorithms</em>).</p>

<p>But why, in actual fact, is biological plausibility important in the first place?
Why do so many sources qoute such argument, and what are the advantages of biological plausibility, when related to machine learning applications?</p>

<p>I have searched a lot, but haven't really seen anyone answer this specifically.</p>

CC BY-SA 3.0
2018-01-07T01:37:37.577Z
This question is probably a little better suited to https://ai.stackexchange.com/ since your ultimate objective is just a better artificial system. There's a reason they have a [machine learning](https://ai.stackexchange.com/questions/tagged/machine-learning) tag but there's not one here.
got trolled too much this week
CC BY-SA 3.0
2018-01-07T01:52:25.887Z
Related question: https://psychology.stackexchange.com/questions/6451/how-distantly-related-are-research-in-computational-neuroscience-and-neural-netw
got trolled too much this week
<neuroscience><learning><neural-network><plasticity>
Why is Biological plausibility in Machine Learning important?
WiseDev
# Why is Biological plausibility in Machine Learning important?

(Asked by: username_0 on 2018-01-06)

I have found that many authors of machine learning papers, that employ the *Hebbian learning* rule, refer to the **biological plausibility of it**, as one of the arguments to use it instead of the well known back-propagation algorithm
([Whittington & Bogacz, 2017](https://pdfs.semanticscholar.org/67d3/93763869e67874f61b4f13b95ae847e55e55.pdf); [Pugh, Soltoggio, & Stanley, 2014](http://eplex.cs.ucf.edu/papers/pugh_alife14.pdf); [Burms, Caluwaerts, &
Dambre, 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4538293/)) (*I imagine the same argument is used for other biologically plausible algorithms*).

But why, in actual fact, is biological plausibility important in the first place?
Why do so many sources qoute such argument, and what are the advantages of biological plausibility, when related to machine learning applications?

I have searched a lot, but haven't really seen anyone answer this specifically.

**Tags:** neuroscience, learning, neural-network, plasticity

> **Comments:**
> **username_1**: This question is probably a little better suited to https://ai.stackexchange.com/ since your ultimate objective is just a better artificial system. There's a reason they have a [machine learning](https://ai.stackexchange.com/questions/tagged/machine-learning) tag but there's not one here.  
> **username_1**: Related question: https://psychology.stackexchange.com/questions/6451/how-distantly-related-are-research-in-computational-neuroscience-and-neural-netw  

---

## 1 answers

### Answer by username_2 on 2018-01-07. Score: 2

They're mentioning biological plausibility, because they think biological constraints are important for intelligent systems. Also, biologically constrained systems could be easily applied to understanding the brain. Whether biological plausibility is important or not to developing intelligent systems and understanding the brain is a matter of debate.

On one hand, there's the argument that since the brain is the only form of intelligence we know, we should probably imitate it to develop intelligent systems. Also, at the same time, we would learn to understand the brain!

On the other hand, there's the often-repeated argument that we did not get airplanes from imitating birds, but from understanding the principles of aerodynamics. In turn, the principles of aerodynamics were then applied to bird wings and feathers.

For better introduction to this debate applied to understanding the brain and creating scalable intelligence, try reading "[Marr's Attacks: On Reductionism and Vagueness](http://onlinelibrary.wiley.com/doi/10.1111/tops.12133/abstract)" by Eliasmith et al.


---

