<p>Although the effects of BCM and Oja's learning rule have <a href="https://pythonhosted.org/nengo/examples/learn_unsupervised.html" rel="nofollow noreferrer">been explored superficially in a computational manner</a> in the context of the NEF, judging from the results (which I've reproduced below) it isn't clear to me if they can/should be combined in any meaningful way.</p>

<p><a href="https://i.sstatic.net/Y3pMC.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Y3pMC.png" alt="oja and bcm"></a></p>

<p>The best hypothesis that I can come up with is BCM forces the ensemble to be either "ON" or "OFF" by minimising transitory states, while Oja provides limits to how much the connection weights can change. So in a way, Oja provides bounds to BCM? Further investigation is needed.</p>

CC BY-SA 3.0
2016-08-19T17:38:03.850Z
2016-08-19T17:38:03.850Z
Seanny123
<p>A software I'm using has implemented two unsupervised learning algorithms, Oja's and Bienenstock, Cooper, Munro's (BCM) learning rule. I understand that they are two very different algorithms for adjusting a neuron's connection weight, but I was wondering what is the relation between them. Do they both have the same plausibility? Can they be used together or are they mutually exclusive?</p>

<learning><theoretical-neuroscience><computational-modeling>
What's the relation between BCM and Oja's learning rule?
Seanny123
# What's the relation between BCM and Oja's learning rule?

(Asked by: username_0 on 2014-10-15)

A software I'm using has implemented two unsupervised learning algorithms, Oja's and Bienenstock, Cooper, Munro's (BCM) learning rule. I understand that they are two very different algorithms for adjusting a neuron's connection weight, but I was wondering what is the relation between them. Do they both have the same plausibility? Can they be used together or are they mutually exclusive?

**Tags:** learning, theoretical-neuroscience, computational-modeling


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## 1 answers

### Answer by username_0 on 2016-08-19. Score: 1 | Accepted answer

Although the effects of BCM and Oja's learning rule have [been explored superficially in a computational manner](https://pythonhosted.org/nengo/examples/learn_unsupervised.html) in the context of the NEF, judging from the results (which I've reproduced below) it isn't clear to me if they can/should be combined in any meaningful way.

[![oja and bcm](https://i.sstatic.net/Y3pMC.png)](https://i.sstatic.net/Y3pMC.png)

The best hypothesis that I can come up with is BCM forces the ensemble to be either "ON" or "OFF" by minimising transitory states, while Oja provides limits to how much the connection weights can change. So in a way, Oja provides bounds to BCM? Further investigation is needed.


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