What learning scientists think brain actually uses? Back-propagation and such seem like a method god would use to architect static brain for given task.
Backprop falls within the class of 'supervised learning' which can indeed be said not to be very biologically realistic. However, reinforcement learning is observed, so the overall picture is probably much more complex: e.g. associative/recurrent/etc networks with Hebb-like unsupervised learning developing neuronal group testing and selection systems that involve reinforcement learning. (see first lecture/talk in [3].)
Perhaps worth a watch is a very nice talk by Geoffrey Hinton [2], which is oft referred to on HN. (Hinton does refer to the notion of biological plausibility etc. in this talk as far as I recall, but the focus is elsewhere (developing next generation state-of-the-art (mostly unsupervised) machine learning techniques/systems.))
[3]: http://kostas.mkj.lt/almaden2006/agenda.shtml (The original summary HTML file is gone from the original source, so this is a mirror; the links to videos and slides do work, though.) The first and the second talks are somewhat relevant (particularly the first one, re: bio plausibility etc ("Nobelist Gerald Edelman, The Neurosciences Institute: From Brain Dynamics to Consciousness: A Prelude to the Future of Brain-Based Devices")), but all are great. Rather heavy, though. (Also, skip the intros.)
edit that first talk/lecture from Almaden (Edelman's) is actually a very nice exposure of the whole paradigm in which {cognitive,computational,etc} neuroscience rests; it does get hairy later on; overall, it's a great talk for the truly curious.
Backprop falls within the class of 'supervised learning' which can indeed be said not to be very biologically realistic. However, reinforcement learning is observed, so the overall picture is probably much more complex: e.g. associative/recurrent/etc networks with Hebb-like unsupervised learning developing neuronal group testing and selection systems that involve reinforcement learning. (see first lecture/talk in [3].)
Perhaps worth a watch is a very nice talk by Geoffrey Hinton [2], which is oft referred to on HN. (Hinton does refer to the notion of biological plausibility etc. in this talk as far as I recall, but the focus is elsewhere (developing next generation state-of-the-art (mostly unsupervised) machine learning techniques/systems.))
[1]: https://en.wikipedia.org/wiki/Hebbian_theory
[2]: https://www.youtube.com/watch?v=AyzOUbkUf3M
[3]: http://kostas.mkj.lt/almaden2006/agenda.shtml (The original summary HTML file is gone from the original source, so this is a mirror; the links to videos and slides do work, though.) The first and the second talks are somewhat relevant (particularly the first one, re: bio plausibility etc ("Nobelist Gerald Edelman, The Neurosciences Institute: From Brain Dynamics to Consciousness: A Prelude to the Future of Brain-Based Devices")), but all are great. Rather heavy, though. (Also, skip the intros.)
edit that first talk/lecture from Almaden (Edelman's) is actually a very nice exposure of the whole paradigm in which {cognitive,computational,etc} neuroscience rests; it does get hairy later on; overall, it's a great talk for the truly curious.