Physiological basis of memory

Stephen Downes has a fascinating post about the science behind memory, summarising a paper by Nobel prize-winner Eric Kandel on Genes, synapses and memory storage [PDF]– and exploring the implications for learning. It really is excellent and you should read both his post and the original paper.

From studies of Aplysia (the sea slug, one of those classic over-researched model species, like E. coli, Arabidopsis, Drosophila, lab rats and mice, Rhesus monkeys, and Psychology students) Kandel draws out two forms of memory:

  • Short-term storage for implicit memory involves functional changes in the strength
    of pre-existing synaptic connections.
  • Long-term storage for implicit memory involves the synthesis of new protein and the growth of new connections

Stephen takes Kandel’s distinction- that ‘Learning refers to the acquisition of new information about the world and memory refers to the retention of that information over time’ – to mean that:

  • Learning is a semantic process. It is about things. It has meaning.
  • Memory is a syntactic process. It is a set of mechanisms. It may or may not have meaning.

As he says, this is a difficult distinction, and I’m really not sure I agree with it in principle. From the biochemistry we know that learning (almost by definition, actually) takes place in relation to one or more stimuli. That doesn’t, to my mind, require that the learning is meaningful. The associations can be entirely arbitrary. Stephen puts it well when he says that “learning is associative, not propositional”. So is memory.

Learning is certainly related to something, but the transduction of external stimuli in to synaptic changes in the brain is far from direct, and when you get in to associative learning it’s even more complex than that.

I think Stephen may be arguing that only learning can be meaningful, in the sense of referring accurately to the external world. Since the transfer to memory is a separate process, there is a potential loss of accuracy, and hence meaning.

I see two problems with that. Firstly, one can imagine that meaning could arise from the combination of separate learning experiences. It’s only after many encounters with fluffy objects that a baby can understand the difference between a soft toy (that can be safely squeezed or bitten) and a cat (which will hiss and scratch if mistreated). The individual observations make a lot more sense when related to each other. And note that this understanding could be wrong – for instance, the heuristic the child uses to distinguish the two may only work for a limited subset of cats, toys and locations they are found in.

Secondly, and more fundamentally, I think the very concept of ‘meaning’ and ‘sense-making’ are not compatible with the level of description we’re dealing with here. Meaning is a complex, socially-mediated thing. Membrane depolarisation, glutamate release and protein synthesis are much less so. (As an aside, this is related to my deep lack of faith in the larger claims of the Semantic Web project.)

We’re making huge progress in linking that hard-science base to the more directly socially-useful stuff about learning, as that scientific understanding expands hugely. Things like the increasing ubiquity of fMRI apparatus is transforming our understanding of what’s going on physiologically when learning happens. But I don’t think it will ever be possible to straightforwardly and easily move from synapses to semantics, from neurons to meaning.

I’ve an argument brewing for why it’s actually impossible, not just difficult and complex … but that’s for later.

Author: dougclow

Data scientist, tutxor, project leader, researcher, analyst, teacher, developer, educational technologist, online learning expert, and manager. I particularly enjoy rapidly appraising new-to-me contexts, and mediating between highly technical specialisms and others, from ordinary users to senior management. After 20 years at the OU as an academic, I am now a self-employed consultant, building on my skills and experience in working with people, technology, data science, and artificial intelligence, in a wide range of contexts and industries.

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