Learning objectives explore the number of memories that can be stored within a network of neurons using the model of autoassociative networks explain why even though Hopfield networks work well in artificial applications this learning rule is not used in the brain ID: 779190
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Slide1
Capacity of auto-associative networks
Learning objectives:
explore the number of “memories” that can be stored within a network of neurons, using the model of auto-associative networks
explain why even though Hopfield networks work well in artificial applications, this learning rule is not used in the brain
Slide2“Has it ever struck you…that life is all memory, except for the one present moment that goes by you so quickly you hardly catch it going? It’s really all memory…except for each passing moment.”
~ Tennessee Williams
Slide3Donald
Hebb
Neurons that fire together, wire together.
Slide4Memory fundamentals
Slide5Terje
Lomo
and Tim Bliss’s investigation of the rat hippocampus
Slide6After stimulation
This was due to the formation of extra post-synaptic
receptors AND more glutamate release.
High-frequency stimulation of the
perforant
path resulted in
increased amplitude of excitatory post-synaptic potentials
Long-term
potentiation
Slide7Increased post-synaptic response
lasts for hours or longer
Slide8Place cells
http://
hargreaves.swong.webfactional.com
/
place.htm
Slide9Place cells
Place field of this
particular place cell
Slide10Mice lose the ability to maintain stable
place fields when LTP is inhibited.
Slide11Memory “fun facts”
Slide12Henry
Molaison
Suffered brain damage in a bicycle accident at age 7, had temporal lobe epilepsy
Had his entire hippocampus removed at age 27, which largely cured his epilepsy…
…but it left him unable to form new memories!
He could remember what happened before the operation, but could not form
memories for facts or events afterward
He could remember information for a few minutes, but could not transfer that information
into long-term memory
Slide13Memento
Slide14Slide15Principles of Neural Science
,
Kandel
, p. 1446
HM could form new long-term
implicit
memories
Slide16Different forms of memory
Principles of Neural Science
,
Kandel
, p. 1447
Slide17Howard Engel
Mystery author
Stroke left him unable to read
Re-learned
how to read by tracing the shapes of words on the roof of his mouth with his tongue, and associating the movement with the spoken word!
Slide18Modeling the CA3 as
an auto-associative network
Section 4.7,
Tutorial on Neural Systems Modeling
, Anastasio
Slide19Unbiological
Slide20If the Hopfield rule works so well, why doesn’t the brain use it?
We’ll explore this in the lab
Slide21What if the change in weights doesn’t have to be 1?
Slide22Different learning rules are better
for different pattern
sparsities
As the patterns become more sparse, the optimal learning rule is the
Hebbian
rule.
Slide23Neuronal activity in the hippocampus is extremely sparse
Slide24CA3 features recurrent connections
When LTP is knocked out at these specific synapses,
mice perform worse in specific memory tasks.
Slide25Further experimental investigation is still required to confirm that the CA3 does function as an auto-associative neural network.
Slide26The hippocampus helps consolidate memory traces in the cortex
“The Organization of Recent and Remote Memories,” PW Franklin and B
Bontempi
,
Nature Rev.
Neurosi
.
Slide27Memory traces are thought to be transferred from the hippocampus to the cortex during sleep
“The memory function of sleep,” S
Diekelman
and J Born,
Nature Rev.
Neurosci
.
Slide28To the lab!