[DennisP]

The brain seems to be pretty darn plastic. There have been experiments with giving people whole new sensory inputs, and finding that the brain adapted to them quite well. Two examples:

- A grid of electrodes on the tongue, activating with a pattern fed from a camera. After a while, blind experimental subjects reported actually seeing what the grid displayed.

- A belt of buzzers hooked to a compass. The buzzer closest to magnetic north was always active. After a couple months, subjects weren't really conscious of the buzzing, but got a really good mental map of their environment and their position in it. They could navigate unfamiliar environments much better than before...and then much worse, when the experiment ended.

There are also some well-known cases of blind people using echolocation pretty effectively, without any special hardware.

Don't have links handy, unfortunately.

[iamthepieman]

This American Life did an episode recently on Daniel Kish, one of the most famous echo location practitioners[0] and here's a short youtube video of the same guy.[1]

[0]http://www.thisamericanlife.org/radio-archives/episode/544/b...

[1]https://www.youtube.com/watch?v=A8lztr1tu4o

[zersiax]

Echolocation is a skill that basically translates clicky sounds that bounce back into structural details of the obstruction the sound waves encounter. be it height, weight, distance and if you get good at it even material composition. Look at Dan Kish to see an example

[tormeh]

This. Make any prediction of how advanced the brain is, then double that, at the very very least. It is barely short of magical. Even single neurons can do processing way more advanced than the artificial ones we use in machine learning. The rest of the body is honestly quite disappointing in comparison. Even the immune system and DNA/RNA mechanisms seem trivial and those are pretty dope compared to most other things.

[sfilipov]

Even single neurons can do processing way more advanced than the artificial ones we use in machine learning.

How does that work? A single neuron is a single neuron. My understanding is that the brain has lots of neurons and also they are assembled in certain "NN architectures" that are far more advanced than what we currently have. But I think that if you go the the single neuron level then they are pretty similar in terms of problem solving capabilities.

[tormeh]

Well, a neuron can either fire or not fire; 0 or 1. For a neuron to fire, enough of its' synapses need to fire. How likely a single neuron is to do so based on an incoming action potential varies over time according to Hebbian learning. Once x synapses fire, the input is linearly summed to determine if the neuron as a whole fires. That's a neat abstraction and it is pretty descriptive. But it's also a little too neat to be true. The summation is, of course, not linear and neurons react to incoming signals even if they don't fire. How likely a neuron is to fire also depends on how much it has fired recently, as the synapses "get tired" (the concentration of certain molecules are temporarily exhausted), but for some neurons, likelihood increases as a result of previous firings before it decreases. Hebbian learning increases the strength of the connection to neurons that participates in successfully firing the neuron, but connections under a certain strength threshold decay with time, while those over it are stable (inability to create these stable connections is connected to alzheimers).

That's what I remember and I just took some neuroscience courses. There are multiple books out there attempting only to describe behaviour mathematically, let alone describing the underlying mechanisms, which is true "here be dragons" territory.