I see a lot of criticism here saying things like "DNNs have nothing to to with brains, they weren't designed to work like brains, and any resemblance is surely just an artifact of training them to do brain-like things."<p>The fact is, there have been neuroscientists working with neural network models with greater and lesser complexity than DNNs for decades. They've been utilized to great profit outside of neuroscience lately, but that doesn't make them not an abstraction of some aspects of cortical computation.<p>We don't quite understand how brains could perform or approximate backprop yet, but it's the only training algorithm that has been remotely successful at training networks deep enough to do human-like visual recognition. So many people take that as a big clue as to what we should be looking for in the brain to explain its great performance and ability to learn, rather than a reason to disqualify DNNs entirely.<p>There's plenty of modeling work going on with more traditional biophysical models, such as those that include spiking, interneuron compartments, attractor dynamics, etc. This is just an attempt to also come at the problem from the other direction, starting from something that we know works well (for vision) and trying to figure out how to ground it in biophysical reality.
function optimization in deep learning sense has nothing to do with neuroscience, I hope they don't think of fitting this model to brain processes just because it's popular
I find it disappointing that the paper makes no mention of Numenta, TBToI or HTM. How is what they are proposing not already included in Numenta's work (informally, of course)? Plus, Numenta's work seems to go much further confronting biological plausibility head-on.
My 8 month old daughter suffers from cortical visual impairment after contracting bacterial meningitis caused by e coli during the birthing process. She had to have a bilateral cranitomy to have isolated areas of the infection carved out of her brain tissue.<p>Looking at this article, i wonder if we'll ever be able to figure any of this out. I feel pretty hopeless about the entire situation.
This might be a little late, but it may be helpful:<p><a href="https://singularityhub.com/2019/10/03/deep-learning-networks-cant-generalize-but-theyre-learning-from-the-brain/" rel="nofollow">https://singularityhub.com/2019/10/03/deep-learning-networks...</a>
I'm not sure, but I think it's trying to say that deep learning as it stands is modeled on one aspect of a model of the brain, by developing out the 3 aspects they identify and having them act in unison would be potentially a good thing, disclaimer, I am neither a neuroscientist nor deep learning expert!