<p>Yes and no.</p>

<p>Neural networks that use edge weights simulate synaptic plasticity - a key mechanism in the brain operation. But it isn't the only one.</p>

<p>In practice, cognition is the combination of various mechanisms, some we are still unsure of.</p>

<p>Perhaps the most obvious missing ingredient of weighted networks is the lack of <strong>frequency/time</strong> domains similar to that affecting the brain. Neurons involve time-dependent operation - for instance, both the <strong>action potential</strong> and the following <strong>refractory period</strong> are time-bound. The brain also involves <strong>synchrony</strong>, e.g. the phase-locked discharges of a neuron set.</p>

<p>There're also issues like different axon lengths that play a part. And many more.</p>

<p>In addition, and possibly above all, the actual <strong>topography</strong> matters a great deal - how everything is connected.</p>

<p>More advance brain simulations involve many of these extra components - mimicking more accurately a real biological brain.</p>

CC BY-SA 3.0
2015-12-02T17:25:24.103Z
Recurrent Neural networks use delay operators to allow a gap before the feedback reaches. The neuron doesn't activate next time until the feedback. Isn't that quite close to the refractory period of a cell?
kneelb4darth
CC BY-SA 3.0
2015-12-02T17:33:01.227Z
@kneelb4darth - Absolutely. My answer applies to neural networks of the most basic kind - those based on edge weights only. There are a multitude of factors you can incorporate to make it more brain-like. Delay operators are just one.
Izhaki
CC BY-SA 3.0
2015-12-02T17:41:09.680Z
Could you please expand a bit on the synchrony part. Googling it didn't get me much :)
kneelb4darth
CC BY-SA 3.0
2015-12-02T18:43:01.117Z
@kneelb4darth Take a look at https://en.wikipedia.org/wiki/Neural_oscillation#Phase_resetting
Justas
CC BY-SA 3.0
2015-12-02T19:48:27.260Z
@kneelb4darth, this is an excellent answer on the topic: [Importance of Neural Synchrony to Cognition](http://cogsci.stackexchange.com/a/7770/3330)
Izhaki
CC BY-SA 3.0
2015-12-02T16:23:48.450Z
2015-12-02T16:23:48.450Z
Izhaki
<p>The approach of the artificial neural networks that you describe and their application is called Connectionism. There are a number of cognitive architectures that have used this approach to explain cognition, such as <a href="https://en.wikipedia.org/wiki/Leabra" rel="nofollow">Leabra</a>.</p>

<p>The question about whether this is really what the brain is doing is another question entirely. Does the brain do back-propagation? <a href="http://arxiv.org/pdf/1502.04156.pdf" rel="nofollow">This is currently up for debate</a>. Does it contain structures such as LSTMs and other Recurrent Neural Networks? Probably not, as these aren't very efficient uses of memory and neurons. For a further discussion of biological plausibility, I would recommend reading "How to Build a Brain" by Chris Eliasmith.</p>

CC BY-SA 3.0
2015-12-02T19:03:46.253Z
2015-12-02T19:10:18.457Z
2015-12-02T19:10:18.457Z
Seanny123
<p>I recently got into Neural networks. As much as I have understood, the learning process is based on the change in weights according to stimulus and algorithm used in learning. Does this in any way represent the real learning process of our brain?</p>

<cognitive-neuroscience><theoretical-neuroscience><neural-network>
Neural nets for cognition
kneelb4darth
# Neural nets for cognition

(Asked by: username_0 on 2015-12-02)

I recently got into Neural networks. As much as I have understood, the learning process is based on the change in weights according to stimulus and algorithm used in learning. Does this in any way represent the real learning process of our brain?

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


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## 2 answers

### Answer by username_1 on 2015-12-02. Score: 1

Yes and no.

Neural networks that use edge weights simulate synaptic plasticity - a key mechanism in the brain operation. But it isn't the only one.

In practice, cognition is the combination of various mechanisms, some we are still unsure of.

Perhaps the most obvious missing ingredient of weighted networks is the lack of **frequency/time** domains similar to that affecting the brain. Neurons involve time-dependent operation - for instance, both the **action potential** and the following **refractory period** are time-bound. The brain also involves **synchrony**, e.g. the phase-locked discharges of a neuron set.

There're also issues like different axon lengths that play a part. And many more.

In addition, and possibly above all, the actual **topography** matters a great deal - how everything is connected.

More advance brain simulations involve many of these extra components - mimicking more accurately a real biological brain.

> **Comments:**
> **username_0**: Recurrent Neural networks use delay operators to allow a gap before the feedback reaches. The neuron doesn't activate next time until the feedback. Isn't that quite close to the refractory period of a cell?  
> **username_1**: @username_0 - Absolutely. My answer applies to neural networks of the most basic kind - those based on edge weights only. There are a multitude of factors you can incorporate to make it more brain-like. Delay operators are just one.  
> **username_0**: Could you please expand a bit on the synchrony part. Googling it didn't get me much :)  
> **username_2**: @username_0 Take a look at https://en.wikipedia.org/wiki/Neural_oscillation#Phase_resetting  
> **username_1**: @username_0, this is an excellent answer on the topic: [Importance of Neural Synchrony to Cognition](http://cogsci.stackexchange.com/a/7770/3330)  

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### Answer by username_3 on 2015-12-02. Score: 1

The approach of the artificial neural networks that you describe and their application is called Connectionism. There are a number of cognitive architectures that have used this approach to explain cognition, such as [Leabra](https://en.wikipedia.org/wiki/Leabra).

The question about whether this is really what the brain is doing is another question entirely. Does the brain do back-propagation? [This is currently up for debate](http://arxiv.org/pdf/1502.04156.pdf). Does it contain structures such as LSTMs and other Recurrent Neural Networks? Probably not, as these aren't very efficient uses of memory and neurons. For a further discussion of biological plausibility, I would recommend reading "How to Build a Brain" by Chris Eliasmith.


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