<p>The question is resolved :</p>

<p><strong>The name HMAX</strong> ( Hierarchical Model And X ) was coined by “Mike Tarr”, who wrote the “News and Views” accompanying the paper in Nature Neuroscience.</p>

<p><strong>What was meant by Hierarchical?</strong></p>

<p>The model has a hierarchical architecture : it contains different stages (layers). </p>

<p>-- Increase in receptive field size and complexity in unit feature preferences along the<br>
   hierarchy.</p>

<p><strong>What was meant by X?</strong></p>

<p>I think it is the pooling operation (it refers to the MAX).</p>

<p><strong>Selectivity in S layer</strong></p>

<p>The activity of each S unit is highest if all the afferents have the specified values.</p>

<p><strong>Invariance in C layer</strong></p>

<p>In contrast to S units, the C cells pool over their afferents, and the response is high if 
one of the afferents is high. This increases invariance if the afferents are tuned to the 
same feature, but at different positions or scales. </p>

CC BY-SA 3.0
2014-01-03T17:33:23.930Z
2014-01-03T17:33:23.930Z
Christina
<p>The human brain can achieve a remarkable ability to recognize visual patterns in an Invariant, selective and fast manner. The human visual system is quite powerful. It has an exquisite selectivity that allows us to distinguish among very similar objects. In addition, iy also allows Robustness (Invariance) to transformations.</p>

<p>In the model (Ventral stream pathway and architecture proposed by Poggio's group) shown in the image below, why the model is selective in S layers and invariant in C layers ?
I didn't understand well why the S layer achieves selectivity and C layer achieves invariance. Please can someone explain to me in details why the model is selective in S and Invariance in C layers?</p>

<p>And who know the HMAX model, the model is called "Hierarchical Model and X". why X? what was meant by "X" ?</p>

<p><img src="https://i.sstatic.net/ZlFnp.png" alt="enter image description here">
This figure is from Serre et al.'s <a href="http://www.ncbi.nlm.nih.gov/pubmed/17925239" rel="noreferrer">A quantitative theory of immediate visual recognition</a>. Prog Brain Res. 2007.</p>

<p>Any help will be very very appreciated.</p>

CC BY-SA 3.0
2014-01-01T21:49:44.563Z
Here's a [free version](http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07_wfig.pdf) of the paper.  I'm still working through it, myself.  My impression is that you basically can always rely on objects being made up of edges with orientations, so they're fixed (invariant) inputs, but each object is an emergent combination of the fixed inputs, so they need selectivity to distinguish particular emergent shapes from other emergent shapes at higher abstraction levels, but still be able to recognize the object in different perspectives or configurations so S can't be too invariant.
Keegan Keplinger
CC BY-SA 3.0
2014-01-01T23:10:44.767Z
Thank you very much for your explanation. Are you working in HMAX model ? if yes, so I can help you :)
Christina
CC BY-SA 3.0
2014-01-03T04:43:00.393Z
No, it was just an interesting question so I wanted to read about it.  Not finding a lot of time lately though.
Keegan Keplinger
CC BY-SA 3.0
2014-01-03T17:33:57.510Z
You can see my response below :)
Christina
<vision><cognitive-neuroscience><computational-modeling><theoretical-neuroscience><neural-network>
Computational model of biological object recognition
Christina
# Computational model of biological object recognition

(Asked by: username_0 on 2014-01-01)

The human brain can achieve a remarkable ability to recognize visual patterns in an Invariant, selective and fast manner. The human visual system is quite powerful. It has an exquisite selectivity that allows us to distinguish among very similar objects. In addition, iy also allows Robustness (Invariance) to transformations.

In the model (Ventral stream pathway and architecture proposed by Poggio's group) shown in the image below, why the model is selective in S layers and invariant in C layers ?
I didn't understand well why the S layer achieves selectivity and C layer achieves invariance. Please can someone explain to me in details why the model is selective in S and Invariance in C layers?

And who know the HMAX model, the model is called "Hierarchical Model and X". why X? what was meant by "X" ?

![enter image description here](https://i.sstatic.net/ZlFnp.png)
This figure is from Serre et al.'s [A quantitative theory of immediate visual recognition](http://www.ncbi.nlm.nih.gov/pubmed/17925239). Prog Brain Res. 2007.

Any help will be very very appreciated.

**Tags:** vision, cognitive-neuroscience, computational-modeling, theoretical-neuroscience, neural-network

> **Comments:**
> **username_1**: Here's a [free version](http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07_wfig.pdf) of the paper.  I'm still working through it, myself.  My impression is that you basically can always rely on objects being made up of edges with orientations, so they're fixed (invariant) inputs, but each object is an emergent combination of the fixed inputs, so they need selectivity to distinguish particular emergent shapes from other emergent shapes at higher abstraction levels, but still be able to recognize the object in different perspectives or configurations so S can't be too invariant.  
> **username_0**: Thank you very much for your explanation. Are you working in HMAX model ? if yes, so I can help you :)  
> **username_1**: No, it was just an interesting question so I wanted to read about it.  Not finding a lot of time lately though.  
> **username_0**: You can see my response below :)  

---

## 1 answers

### Answer by username_0 on 2014-01-03. Score: 2 | Accepted answer

The question is resolved :

**The name HMAX** ( Hierarchical Model And X ) was coined by “Mike Tarr”, who wrote the “News and Views” accompanying the paper in Nature Neuroscience.

**What was meant by Hierarchical?**

The model has a hierarchical architecture : it contains different stages (layers).

-- Increase in receptive field size and complexity in unit feature preferences along the  
hierarchy.

**What was meant by X?**

I think it is the pooling operation (it refers to the MAX).

**Selectivity in S layer**

The activity of each S unit is highest if all the afferents have the specified values.

**Invariance in C layer**

In contrast to S units, the C cells pool over their afferents, and the response is high if
one of the afferents is high. This increases invariance if the afferents are tuned to the
same feature, but at different positions or scales.


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