Did you just see that other post about Cephalopod eye anatomy and write this?
I ask because you have a poor grasp over what evolution actually is when you say things like “evolution made a mistake”. The truth is that our eyes are one of many, many layouts in the animal kingdom, it’s not some binary thing like you’re making it out to be.
I actually came across this for the first time when I was doing research into the visual pathway for the purpose of trying to structure a spiking neural net more closely to human visual processing.
The Wikipedia page mentions cephalopod eyes specifically when talking about the inverted retina of vertebrates.
The vertebrate retina is inverted in the sense that the light-sensing cells are in the back of the retina, so that light has to pass through layers of neurons and capillaries before it reaches the photosensitive sections of the rods and cones.[5] The ganglion cells, whose axons form the optic nerve, are at the front of the retina; therefore, the optic nerve must cross through the retina en route to the brain. No photoreceptors are in this region, giving rise to the blind spot.[6] In contrast, in the cephalopod retina, the photoreceptors are in front, with processing neurons and capillaries behind them. Because of this, cephalopods do not have a blind spot.
The Wikipedia page goes on to explain that our inverted retinas could be the result of evolution trying to protect color receptors by limiting their light intake, as it does appear that our glial cells do facilitate concentrating light.
However, the “positive” effects of the glial cells coming before the receptors could almost certainly be implemented in a non-inverted retina. So that’s the evolutionary duct tape I was mentioning.
It would be difficult to flip the retina back around (in fact since it originates as part of the brain we’d kind of have to grow completely different eyes), so that’s not an option for evolution.
However, slight changes to the glial cells and vasculature of the eyes is definitely more possible. So those mutations happen and evolution optimizes them as best it can.
Evolution did well to optimize a poorly structured organ but it’s still a poorly structured organ.
SNNs more closely resemble the function of biological neurons and are perfect for temporally changing inputs. I decided to teach myself rust at the same time I learned about these so I built one from scratch trying to mimic the results of this paper (or rather a follow up paper in which they change the inhibition pattern leading to behavior similar to a self organizing map; I can’t find the link to said paper right now…).
After building that net I had some ideas about how to improve symbol recognition. This lead me down a massive rabbit hole about how vision is processed in the brain and eventually spiraled out to the function and structure of the hippocampus and now back to the neocortex where I’m currently focusing now on mimicking the behavior and structure of cortical minicolumns.
The main benefit of SNNs over ANNs is also a detriment: the neurons are meant to run in parallel. This means it’s blazing fast if you have neuromorphic hardware, but it’s incredibly slow and computationally intense if you try to simulate it on a typical machine with von Neumann architecture.
Did you just see that other post about Cephalopod eye anatomy and write this?
I ask because you have a poor grasp over what evolution actually is when you say things like “evolution made a mistake”. The truth is that our eyes are one of many, many layouts in the animal kingdom, it’s not some binary thing like you’re making it out to be.
I actually came across this for the first time when I was doing research into the visual pathway for the purpose of trying to structure a spiking neural net more closely to human visual processing.
The Wikipedia page mentions cephalopod eyes specifically when talking about the inverted retina of vertebrates.
The Wikipedia page goes on to explain that our inverted retinas could be the result of evolution trying to protect color receptors by limiting their light intake, as it does appear that our glial cells do facilitate concentrating light.
However, the “positive” effects of the glial cells coming before the receptors could almost certainly be implemented in a non-inverted retina. So that’s the evolutionary duct tape I was mentioning.
It would be difficult to flip the retina back around (in fact since it originates as part of the brain we’d kind of have to grow completely different eyes), so that’s not an option for evolution.
However, slight changes to the glial cells and vasculature of the eyes is definitely more possible. So those mutations happen and evolution optimizes them as best it can.
Evolution did well to optimize a poorly structured organ but it’s still a poorly structured organ.
Can you elaborate on that first paragraph? I’m interested.
SNNs more closely resemble the function of biological neurons and are perfect for temporally changing inputs. I decided to teach myself rust at the same time I learned about these so I built one from scratch trying to mimic the results of this paper (or rather a follow up paper in which they change the inhibition pattern leading to behavior similar to a self organizing map; I can’t find the link to said paper right now…).
After building that net I had some ideas about how to improve symbol recognition. This lead me down a massive rabbit hole about how vision is processed in the brain and eventually spiraled out to the function and structure of the hippocampus and now back to the neocortex where I’m currently focusing now on mimicking the behavior and structure of cortical minicolumns.
The main benefit of SNNs over ANNs is also a detriment: the neurons are meant to run in parallel. This means it’s blazing fast if you have neuromorphic hardware, but it’s incredibly slow and computationally intense if you try to simulate it on a typical machine with von Neumann architecture.