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> Actually convnets were inspired by Fukushima's Neocognitron, which was itself inspired by visual cortex. That doesn't contradict what I wrote. ConvNets requ

by trott 9y ago

> Actually convnets were inspired by Fukushima's Neocognitron, which was itself inspired by visual cortex. That doesn't contradict what I wrote. ConvNets require the synchronization of weights between neurons, which is not considered to be biologically plausible. Some aspects of the architecture (the receptive fields, in this case) may well be plausible, with the complete architecture still implausible.

rdlecler1 9y ago
You're focusing too much on the implementation details and assuming that because they're different that it's not equivalent. The secret sauce here is that network topology (and threshold rules), not the implementation details, are largely what determine the functional properties of that network. Show an electrical engineer the circuit diagram of a 4 bit adder and they'll know it's function immediately. Artificial Neural Networks and Artificial Gene Regulatory Networks are the same. The problem with ANNs is that we fail to see the circuitry driving the function because we assume that every w_ij != 0 is functionally relevant. Once you start to strip away non-spurious interactions you start to see topological patterns. I did a lot of work on this problem -- this is not an uneducated guess.
- jamez1 9y ago
  I didn't see a maths major in your background but I'm surprised you seem to dance around how important back-propagation is, and how different it is from what you could find in nature. The major advances are the implementation details, and I think many would consider the network topology research constrained not by our imagination but by our implementations.
- deepnotderp 9y ago
  There are some similarities, but ConvNets really remind me of LeCun's airplane vs bird analogy. There are similarities such as learning very similar intermediate representations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288363/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288363/), but they are also very clearly not doing the exact same thing that a human is. It's a technological emulation, and that's good enough for me.
- visarga 9y ago
  > Artificial Gene Regulatory Networks I've been following AI for years but this is the first time I found such a concept. So basically a cell is like a small neural net with as many neurons as genes, each gene having (chemical) input and outputs signals. That means a cell's DNA is much more dynamic than I previously imagined. It's a self-replicating m.f. computer, that's what it is. We can only dream of similar accomplishments. Previously I was aware of the huge workload carried out by DNA - for every protein in the body, DNA replicates the blueprints - an amazing amount of fine detailed work. It's not just sitting there waiting for reproduction. Seeing it not just as a factory, but also as a neural net is another level.

by trott 9y ago

rdlecler1 9y ago

You're focusing too much on the implementation details and assuming that because they're different that it's not equivalent. The secret sauce here is that network topology (and threshold rules), not the implementation details, are largely what determine the functional properties of that network. Show an electrical engineer the circuit diagram of a 4 bit adder and they'll know it's function immediately. Artificial Neural Networks and Artificial Gene Regulatory Networks are the same. The problem with ANNs is that we fail to see the circuitry driving the function because we assume that every w_ij != 0 is functionally relevant. Once you start to strip away non-spurious interactions you start to see topological patterns. I did a lot of work on this problem -- this is not an uneducated guess.

jamez1 9y ago
I didn't see a maths major in your background but I'm surprised you seem to dance around how important back-propagation is, and how different it is from what you could find in nature. The major advances are the implementation details, and I think many would consider the network topology research constrained not by our imagination but by our implementations.
deepnotderp 9y ago
There are some similarities, but ConvNets really remind me of LeCun's airplane vs bird analogy. There are similarities such as learning very similar intermediate representations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288363/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288363/), but they are also very clearly not doing the exact same thing that a human is. It's a technological emulation, and that's good enough for me.
visarga 9y ago
> Artificial Gene Regulatory Networks I've been following AI for years but this is the first time I found such a concept. So basically a cell is like a small neural net with as many neurons as genes, each gene having (chemical) input and outputs signals. That means a cell's DNA is much more dynamic than I previously imagined. It's a self-replicating m.f. computer, that's what it is. We can only dream of similar accomplishments. Previously I was aware of the huge workload carried out by DNA - for every protein in the body, DNA replicates the blueprints - an amazing amount of fine detailed work. It's not just sitting there waiting for reproduction. Seeing it not just as a factory, but also as a neural net is another level.