Our Coalescing Minds paper had the one learning algorithm hypothesis as one of its assumptions; I wasn’t the neuroscience expert, but my co-author was, and here’s what he wrote about that premise (note that the paper was intended for a relatively popular audience, so the neuroscience detail was kept light):
An adult human neocortex consists of several areas which are to varying degrees specialized to process different types of information. The functional specialization is correlated with the anatomical differences of different cortical areas. Although there are obvious differences between areas, most cortical areas share many functional and anatomical traits. There has been considerable debate on whether cortical microcircuits are diverse or canonical [Buxhoeveden & Casanova, 2002; Nelson, 2002] but we argue that the differences are variations of the same underlying cortical algorithm, rather than entirely different algorithms. This is because most cortical areas seem to have the capability of processing any type of information. The differences seem to be a matter of optimization to a specific type of information, rather than a different underlying principle.
The cortical areas do lose much of their plasticity during maturation. For instance, it is possible to lose one’s ability to see colors if a specific visual cortical area responsible for color vision is damaged. The adult brain is not plastic enough to compensate for this damage, as the relevant regions have already specialized to their tasks. If the same brain regions were to be damaged during early childhood, color blindness would most likely not result.
However, this lack of plasticity reflects learning and specialization during the lifespan of the brain rather than innate algorithmic differences between different cortical areas. Plenty of evidence supports the idea that the different cortical areas can process any spatiotemporal patterns. For instance, the cortical area which normally receives auditory information and develops into the auditory cortex will develop visual representations if the axons carrying auditory information are surgically replaced by axons carrying visual information from the eyes [Newton & Sur, 2004]. The experiments were carried out with young kittens, but a somewhat similar sensory substitution is seen even in adult humans: relaying visual information through a tactile display mounted on the tongue will result in visual perception [Vuillerme & Cuisiner, 2009]. What first feels like tickling in the tongue will start feeling like seeing. In other words, the experience of seeing is not in the visual cortex but in the structure of the incoming information.
Another example of the mammalian brain’s ability to process any type of information is the development of trichromatic vision in mice that, like mammalian ancestors, normally have a dichromatic vision [Jacobs et al., 2007]. All it takes for a mouse to develop primate-like color vision is the addition of a gene encoding the photopigment which evolved in primates. When mice are born with this extra gene, their cortex is able to adapt to the new source information from the retina and to make sense of it. Even the adult cortical areas of humans can be surprisingly adaptive as long as the changes happen slowly enough [Feuillet et al., 2007]. Finally, Marzullo et al. [2010] demonstrated that rats implanted with electrodes both in their motor and visual cortices can learn to modulate the output from their motor cortex based on feedback given to visual cortex.
Our Coalescing Minds paper had the one learning algorithm hypothesis as one of its assumptions; I wasn’t the neuroscience expert, but my co-author was, and here’s what he wrote about that premise (note that the paper was intended for a relatively popular audience, so the neuroscience detail was kept light):