<p>Is the visual cortex of newborn babies right off the bat capable of making sense of raw visual data, for instance, converting the constant stream of raw RGB images perceived by the eyes into a meaningful higher-level representation of objects in motion in a 3D world? Yes? No? If not, then does it mean this skill has to be developed over time by means of some <strong>learning mechanism</strong>?</p>

<p>If the visual cortex needs time to learn advanced visual skills, how does the actual learning mechanism work? Does the visual cortex have to optimize the connections between neurons, in a way analogous to how artificial neural networks optimize their parameters through backpropagation algorithm (machine learning)? If this is the case, then where does the visual cortex get its error signals from? To make the last question clear, in machine learning the typical approach is to compute the gradient of a loss function which compares the model's prediction with the ground truth, and the model's parameters are updated by moving the parameters in the direction of the gradient. If the visual cortex is learning advanced visual skills by virtue of a similar learning mechanism, then what kind of loss function is the visual cortex optimizing?</p>

CC BY-SA 4.0
2019-04-05T14:54:05.367Z
This isn't on topic here (I am trying to find a better home for it) but what in the world gave you the idea that animals don't need this sort of training? That's precisely what games like [this](http://mommysplaybook.com/wp-content/uploads/2013/11/Shape-Sorter-1024x610.jpg) are for, for example. And that's one of the benefits of play in young mammals of all species.
terdon
CC BY-SA 4.0
2019-04-05T15:11:04.593Z
I'm voting to close this question as off-topic because it does not appear to be about bioinformatics. Maybe biology.SE?
Kamil S Jaron
CC BY-SA 4.0
2019-04-07T13:42:01.500Z
The question relates to developing better deep learning (neural network [NN]) machinery based on human/animal perception for image recognition (convolution is a classic NN method). This idea has allegedly succeeded before in NN. However, the empirical principles of why a DL NN approach works are often poorly understood, the OP will be performing vast numbers of differential equations, in contrast biological approaches are better understood, so I'm not sure this question is going anywhere in any case.
M__
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2019-04-07T15:08:07.433Z
Just to finally add that the modern rule of thumb in image recognition is that the deeper the NN the better the result. So just increase the depth of the NN by ten-fold.
M__
CC BY-SA 4.0
2019-04-08T08:50:00.713Z
It is a good question, interesting too. Now I see that motivation is very computational, but all the questions are very biological and not sure if there is anybody here with the expertise to answer half of them.
Kamil S Jaron
CC BY-SA 4.0
2019-04-08T08:50:44.240Z
Also, it's super broad. It would be a decent thesis topic.
Kamil S Jaron
CC BY-SA 4.0
2019-04-10T13:50:40.330Z
While the question as is is now more about bioinformatics I think it is too broad to be answerable in this format (we can't answer a thesis in a single answer). Perhaps you have a **particular** question you are having problems with (this seems like too general to be an actual problem you have)
llrs
<machine-learning>
Is the visual cortex of a newborn baby immediately capable of object detection or is this skill learned over time, and if so, how?
# Is the visual cortex of a newborn baby immediately capable of object detection or is this skill learned over time, and if so, how?

(Asked by: username_0 on 2019-04-05)

Is the visual cortex of newborn babies right off the bat capable of making sense of raw visual data, for instance, converting the constant stream of raw RGB images perceived by the eyes into a meaningful higher-level representation of objects in motion in a 3D world? Yes? No? If not, then does it mean this skill has to be developed over time by means of some **learning mechanism**?

If the visual cortex needs time to learn advanced visual skills, how does the actual learning mechanism work? Does the visual cortex have to optimize the connections between neurons, in a way analogous to how artificial neural networks optimize their parameters through backpropagation algorithm (machine learning)? If this is the case, then where does the visual cortex get its error signals from? To make the last question clear, in machine learning the typical approach is to compute the gradient of a loss function which compares the model's prediction with the ground truth, and the model's parameters are updated by moving the parameters in the direction of the gradient. If the visual cortex is learning advanced visual skills by virtue of a similar learning mechanism, then what kind of loss function is the visual cortex optimizing?

**Tags:** machine-learning
> **Comments:**
> **username_1**: This isn't on topic here (I am trying to find a better home for it) but what in the world gave you the idea that animals don't need this sort of training? That's precisely what games like [this](http://mommysplaybook.com/wp-content/uploads/2013/11/Shape-Sorter-1024x610.jpg) are for, for example. And that's one of the benefits of play in young mammals of all species.  
> **username_2**: I'm voting to close this question as off-topic because it does not appear to be about bioinformatics. Maybe biology.SE?  
> **username_3**: The question relates to developing better deep learning (neural network [NN]) machinery based on human/animal perception for image recognition (convolution is a classic NN method). This idea has allegedly succeeded before in NN. However, the empirical principles of why a DL NN approach works are often poorly understood, the OP will be performing vast numbers of differential equations, in contrast biological approaches are better understood, so I'm not sure this question is going anywhere in any case.  
> **username_3**: Just to finally add that the modern rule of thumb in image recognition is that the deeper the NN the better the result. So just increase the depth of the NN by ten-fold.  
> **username_2**: It is a good question, interesting too. Now I see that motivation is very computational, but all the questions are very biological and not sure if there is anybody here with the expertise to answer half of them.  
> **username_2**: Also, it's super broad. It would be a decent thesis topic.  
> **username_4**: While the question as is is now more about bioinformatics I think it is too broad to be answerable in this format (we can't answer a thesis in a single answer). Perhaps you have a **particular** question you are having problems with (this seems like too general to be an actual problem you have)  

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