4 ms·

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

by dmreedy 10y ago

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.

Cybiote 10y ago
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.
Cybiote 10y ago
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/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569491/

by dmreedy 10y ago

Cybiote 10y ago

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.

Cybiote 10y ago

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/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569491/