There have been recent discoveries about synapses as well. It turns out that synapses are pretty complex. Lots of single celled organisms seem to have pretty "smart" behavior for their size. It turns out a lot of that data processing happens around the cell membrane. These mechanisms are the evolutionary roots of synapses.
Indeed, it seems that some kind of 'backpropagation' does happen in the brain, in contrary to what was always believed. This might have impact on machine learning research.
Not really. For instance, people have been looking for 5'-3' DNA polymerase for decades, because it seems like it would be a simpler mechanism for DNA replication than how it really works. But it doesn't seem to exist anywhere.
My statement wasn't meant as a disparagement of biologists. Simply that there is a lot of "conventional wisdom" out there in the world that turns out to be false.
When I started grad school (mol bio/genetics), there was a laundry list of things that "never happened in biology". By the time I finished grad school a lot of those items were removed from the laundry list.
And, as I'm sure you're aware, the inability to find something is not evidence that it doesn't exist.
Basically, when a cell divides, it needs to produce an extra copy of DNA. One for each daughter cell. The DNA in your cells is double stranded, which means it is basically two copies of your DNA stuck together. So each one of those copies needs to be duplicated before the cell divides.
You might imagine that the cell would do this by splitting the two DNA strands and sending some molecular machine down each one to replicate it. That is what it does - kind of. See, the tricky part is that the two DNA strands are pointing in opposite directions. The heads and tails of the nucleotides (A, C, T, or G) in each strand are pointed in opposite directions.
You might think that, if evolution can create a machine that works in one direction, it could create a machine that works in the other direction. Then, one could be used on each strand in parallel. Back in the day (~40 years ago), this is basically what everyone assumed must be happening.
But that's not what happens. We only have a machine that goes in one direction. People spent many, many years looking for these little molecular machines, but only ones that went in the same direction were found. None that went backwards.
So for the backwards strand, it's duplicated in a really convoluted process. Basically, instead of copying it all in one shot, it has to repeatedly jump ahead, work back, jump ahead, work back, creating a bunch of little DNA fragments. While it does this, all the little fragments have to be tied together. It's a very strange process.
It's always seemed intuitively surprising that there's no feedback mechanism within neurons to aid learning. What's the currently favoured mechanism for learning, neurons feeding back to previous neurons?
There's no single mechanism in neuroscience to explain learning in general, because the current understanding is that "learning" is a very vague term that covers many types of adaptation, and each has its own mechanism.
I'm not familiar with any network-level mechanisms, but there are many local (synapse- or dendrite-level) ones. The one I'm most familiar with is spike-timing dependent plasticity (STDP) [1], which modifies the strength of a synapse based on the millisecond-level timing of action potentials. When cell A tends to fire just before cell B, and the two have synapses connecting them, then cell B will increase the strength of its synapses to A. The reverse is true too: if cell A tends to fire just after cell B, then the synapses will decrease in strength. This is a form of Hebbian learning [2].
Yes, recurrent connections are one of the mechanisms that could be responsible for learning. These are used to 'steer' front-end neurons, for example to focus on a certain feature.
There is also 'Hebbian learning', which means that the connections between neurons that fire at the same time become stronger.
http://www.brainsciencepodcast.com/bsp/2008/12/6/surprising-...