HarHarVeryFunny
an hour ago
I wonder how much of this is directly related to age (biological aging), and how much is just someone's brain becoming "full" due to more memories getting added every year?
It seems that memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory that is able to keep memories distinct due to the very high dimensional space (# neurons per assembly) being used. However, you'd expect that at some point if you store too many memories in a single assembly the recall accuracy is going to go down.
You'd expect that with big brains being so costly, evolution has only equipped us with brains big enough to store a lifetime of memories, so it would be odd if memory didn't suffer as we get old.
To make a computer analogy, it's a bit like a hash table getting too full. Say you had a hash table without any overflow mechanism... up to a point recall may still be pretty good, but as the table gets closer to full there will be more hash collisions and likelyhood of "false recall". Obviously the brain is not a computer, but the analogy may hold up reasonably well if you consider the hash table keys and values as embeddings and the store operation being an embedding merge rather than overwrite.
randomImmigrant
40 minutes ago
The brain does not have the Von Neumann bottleneck. Unlike most current digital systems, the brain doesn’t have a separate memory registry it needs to pull from.
Engrams, that is, the physical trace of a memory, are not stable through life. They start out in the hippocampus, but as the stimulus recedes in time without reinforcement, it moves away.
No evidence exists though that the memory is encoded in one set of cells. This spatial segregation of memory is the worst hangover from the “brain is a computer” analogy. Even if it is, why in the world would it be like our digital devices which specifically have the Von Neumann bottleneck? In biology, memory and processing are not segregated.
There’s growing evidence the memory is much more distributed over the network, and is recomposed based on salience overlap with a new stimulus.
Another factor to keep in mind is circadian rhythms. There’s growing evidence for how much the memory system and timekeeping system overlap, at a molecular level. Every neuron (and other cell) has an intrinsic clock that ticks at roughly 24 hours, and continues to do so even in total darkness.
When you encode the memory has a lot to say, based on your chronotype, on how and how well you will remember it. Same with learning: there’s a time of day based variation.
Sleep, and dreaming, is when these memories seem to get replayed and critical features and connections are incorporated into the system and its regime, awaiting the right triggers to access a state similar to when the memory formed.
I’m stitching across a lot of different research, and I want to be clear many aspects of this system are not yet fully worked out.
But what we do know points to a system that works with different physical and algorithmic priors, and the dynamics are sharply distinct from current digital computers.
cheschire
an hour ago
I am not a biologist so I cannot say this authoritatively, but I’m fairly certain that’s not how neuroplasticity works.
HarHarVeryFunny
an hour ago
I only mentioned neural assemblies storing multiple embeddings - what about that contradicts your understanding of neuroplasticity?
dboreham
an hour ago
How so?