Just wanted to add that there _are_ certain portions of the brain which do seem to be layered, namely fundamental image-recognizers in the visual cortex. These were a lot of the inspiration for layered neural nets, and may explain some of their success on similar tasks.

As I point out in another post (currently above) the layers of Neural nets have nothing in common with those in the brain. Only confusion will be met in heading down that path.

What is had in common, and what was inspirational were (harsh) experiments on kittens, done in the 60s which provided hints as to what concepts were important in the primary visual cortex. These experiments lead to ideas of pooling and averaging as ways to calculate invariance, which made their way into the neurocognitron and then the convolutional net. The concepts of thresholding units and summation of inputs also played a part in the designs of Artificial Nets more generally. Most recently, new artificial NN designs have been loosely inspired by the importance of attention in biological networks.

Another similarity, but this is a general computational similarity, is the importance of hierarchical organization in easing learning difficulty. But unlike in the mostly feedforward networks we're used to in ANNs, brain's networks are also feedback, or with backward projections, across areas and up and down hiearchies and so on.

But there is more to vision than V1 and V4. The visual cortex has more arbitrary connections across layers and is not restricted to the bipartite structure common in MLPs and is more fine grained than the hidden layer summation across timesteps of RNNs.

Other things the visual cortex computes but not well captured in models: speed and directional selectivity, nontrivial color transformations, temporal frequency, global v local + foreground v background, optical flow, motion tracking, inhibitory neurons, higher level scene understanding, feedback connections, perceptual color constancy and contributions to memory formation.

Yes, I was specifically referring to the findings from those kitten experiments.

Of course there are plenty of other things that the visual cortex is involved in, as far as we can understand it at least. I'm talking purely about the macrostructures that are involved in elementary vision functions like orientation.

I'm in the camp as well that conflating brains and ANNs is a dangerous and hype-prone line to walk, but I'm not sure you can categorically say that the layers in V1 and the layers in a CNN have nothing in common, with respect to the computational capabilities they model. Likewise with convolutions and columns.

I say that the computations in V1 and the computations in the CNN share a fair bit in common.

However, I doubt that the layers in V1 and CNN have much in common for a couple reasons. First is because the layers in V1 will differ depending on who you're reading from and when they wrote it. The layer divisions are more structural than functional, so far as we know. You can't trace ever more abstract features as you move across V1 layers, not least because V2-V4 are also involved in this.

Next is that V1 sends projections to "higher" and also "lower" areas while connecting to other areas than just the visual. Within V1, connectivity from lower to higher layers are not obvious. Some inputs from the LGN might feed into layer 4 and others to differing layers depending on whether the computation is motion or color etc specific. Layer 5 might also get connections from more distant areas. This level of interconnectivity suggests that very different computations are going on even at the level of just V1. The connections are finer and more intricate than simple bipartite divisions. Basically, they're working out more than just features that are robust to rotations and translations (e.g. speed, direction, dual stream signal integration, oscillations...).

The prevalence and importance of feedback and recurrent connections is not yet well understood but are believed to be important in the sort of high level scene understanding that are still open problems.

And related to that, the notion of inhibition is turning out to be more and more important in biological networks though they aren't really key to any neural nets. Some of their repeating patterns or motifs, have a place in directional and size selectivity but more intriguingly, are figured to be key in the top down flow of information that allows for our (mammalian) complex scene understanding.

The importance and whether there is any function of the column is not without controversy, it may well be an artifact of what tools were used in the early days. The brain is very flexible in its organization and attempts at organizational structures such as columns aren't very consistent even across individuals of the same species: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569491/

I also believe that some[1] believe that CNNs and other image processing networks do some extra work that our brains don't need, as we can "cheat".

For example, we get some translation-invariance for free with the ability to move our eyes.

[1] http://ilp.mit.edu/event.jsp?id=6094 (I had the video URL bookmarked, but it's broken now)

That is a good explanation. I'll point out for anyone that clicks through, that unlike what he says, depth in ANNs do serve a purpose: to make learning more tractable by building representational abstractions.

There are feedback and recurrent connections in the brain but what (2) means to say that the specific mechanism is not biologically plausible. I know there are prominent Deep learning researchers who are trying to work around this but those explanations have never struck me as particularly parsimonious. It's better to start an explanation from experimental data on brains than from them most important aspect of neural nets.