9
$\begingroup$

I'm now reading a book titled Hands-On Machine Learning with Scikit-Learn and TensorFlow and in the Chapter 10 of the book, the author writes the following:

The architecture of biological neural networks (BNN)4 is still the subject of active research, but some parts of the brain have been mapped, and it seems that neurons are often organized in consecutive layers, as shown in Figure 10-2.

enter image description here

However, there seems to be no link to any research there. And the author didn't say it assertively given that he used "it seems that neurons are often organized in consecutive layers".

Is this true and how strongly is it believed? What research is this from?

Improve this question
$\endgroup$
2
  • $\begingroup$ @JadenTravnik's answer is good, also see my comment there. The difference between the reference here and that answer is this citation seems to be implying that there is a feed-forward structure in a single column in neocortex (since that's the picture represented here). This is certainly false, though works as a model sometimes. Although there is evidence for some feed-forward processing in a column, there is also a ton of recurrence and feedback. The feed-forward structure makes more sense between cortical areas (this is what the answer below addresses). $\endgroup$
    – Bryan Krause
    Aug 3, 2017 at 1:58
  • $\begingroup$ The terminology of "layer" has a different meaning in the two contexts as well. When biologists talk about cortical "layers" they mean anatomical layers, not functional neural network-style layers. Cells within one layer are highly interconnected with each other, as well as to a lesser extent with cells of all of the other layers. Some of the connectivity is in this answer to a different question at biology.se: biology.stackexchange.com/questions/57495/… $\endgroup$
    – Bryan Krause
    Aug 3, 2017 at 2:00

1 Answer 1

Reset to default
7
$\begingroup$

Really short answer: yes

Slightly longer answer: kinda

Long answer:

Convolutional neural networks (CNNs), which are now a standard in image processing models, were inspired from work done by Hubel and Wiesel in the 1950-60s. They showed that the visual cortexes of cats and monkeys contain neurons that individually respond to small regions of the visual field.

To give some background, we have to first start from the rods and cones in the eyes. These photosensitive cells are connected to a few layers of cells before even leaving the retina via ganglion cells.

Image of rods connected to bipolar cells connected to ganglion cells

These ganglion cells are then connected to several regions of the brain but primarily the Occipital lobe located at the back of the brain. The Occipital lobe is responsible for visual processing and is separated into cortical layers, the first named V1 which is the primary visual area. Most of the work by Hubel and Wiesel involved cells in V1 and showed how these cells were sensitive to orientation and color from their respective receptive areas on the retina.

enter image description here

The cells in V1 are connected to the cells in V2, which are sensitive to even more specific stimuli, such as movement with orientation, and this trend of specific sensitivity continues up from V2 to higher regions in the brain.

This layered approach to vision has been heavily exploited in CNNs, so much so that when the sensitivity of neurons in trained CNNs is displayed, similar responses (orientation) are found.

enter image description here

There is clear evidence of layers in biological optical systems and similarly layered structures in the other senses. Although there are many connections between different brain structures, the main structure of layers in the brain has helped understand what different areas of the brain do and has helped inspire many (if not all) advances in neural network research.

Improve this answer
$\endgroup$
1
  • 1
    $\begingroup$ Got pinged from Biology.SE. This answer is good, certainly good enough for this field. Layers in real neocortex differ from most neural networks in that they are massively recurrent, consist of simultaneously active feed-forward and feed-back, and very dependent on recent history and overall state. And that's just in one visual area (like V1). Some artificial networks impart some of these features, others mimic them with other more computationally-friendly mechanisms. $\endgroup$
    – Bryan Krause
    Aug 3, 2017 at 1:56

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .