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Sleep Mediation of Episodic Memory and Associative Learning II:
A Potential Computational Synthesis
Itamar Lerner & Mark A. Gluck
for Molecular & Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
Introduction
Qualitative Traits of the Model
Evidence from the last decade shows that sleep has an important role in learning and memory. specifically, sleep – and especially Slow-Wave Sleep (SWS) and,
sometimes, Rapid-Eye-Movement sleep (REM) – has been shown to improve episodic memory, gist extraction, and rule extrapolation and insight. In addition,
it has been shown that following sleep (especially SWS) synaptic strength within cortical and hippocampal circuits is generally decreased, these two findings have
often been taken to support different and even contradicting theories about the role of sleep in learning and memory. The current work in progress is a computational
approach that seeks to combine a broad range of empirical findings within a uniform neuro-computational framework.
Background - I
Parsimonious representations facilitate cognitive performance
After sleep-dependent unification and differentiation, each objective is more readily accessed:
Before sleep:
Learned Test
patterns sample
Gist
extraction:
I. Episodic memory: Paired associates learning
Learned
patterns
Dog - Pianist
Hole - ?
Paired-associates task-design.
Observation of pairs to be
memorized is followed by
12hours of wake or sleep, after
which cued recall is tested.
Slope and Amplitude of Excitatory Post-Synaptic Potentials
(EPSPs) in the prefrontal cortex of rats decrease following sleep
compared to a Sleep-deprivation period. (W – Wake; S – Sleep;
Vyazovskiy et al., 2008)
Changes in cortical Local Field Potential (LFP) in rats in
response to stimulation after a period of wake (Sleep
Deprived - SD) compared to sleep (Liu et al., 2010)
Performance
Plihal &
Born, 1997
Recall performance increases due to SWS between training and testing
II. Gist extraction: Learning meaning of Chinese characters:
1. Based on our previous NSF-supported modeling (Gluck and Myers, 1993; Moustafa et
al., 2009) we assert that storing
episodic memories, extracting gist
information, or extrapolating a
classification rule, all crucially depend
on gradual learning of stimulusMedial Temporal Cortex
Hippocampus
stimulus associations in the
hippocampus during wake. Only after
learning these statistical regularities,
can the system (Medial Temporal
Cortex and Striatum) process
appropriate responses.
A shared pattern in Chinese characters is recognized better after sleep
III. Rule extrapolation: Learning Implicit hierarchy between stimuli
Training:
Memorizing relations between the
item pairs a,b,c,d,e:
a>b, b>c, c>d, d>e, e>f
(pairs contain implicit hierarchy:
a>b>c>d>e>f)
Testing:
Hierarchies with 1° separation: b>d,
d>e
Hierarchies with 2° separation: b>e
Ellenbogen et
al., 2006
Hierarchy rule is more easily
recognized after sleep compared
to wake
• Differentiation: Representations with a small degree
of correlations become largely uncorrelated
• Unification: Representations that are very correlated
to each other are unified to become a single
representation.
3. Both of these changes are carried out by deletion of
synapses: Differentiation is achieved by deletion of
synapses that support activation of neurons common
to several representations (thus causing these
representations to become uncorrelated). Unification
is achieved by deletion of synapses that support
activation of neurons that are unique to each
representation (thus allowing only neurons common
to all these representations to survive, turning these
separate representations into a single
representation).
Rule
learning:
Test
sample
Which of the two
test samples fit
better to the
learned
patterns?
To which of the
two learned
structures does
the test sample
fit?
Gradual learning
during wake
2. Sleep (especially SWS) provides an additional processing stage to the hippocampal
representations that were acquired during wake, allowing them to become more
parsimonious and consequently boost performance in the subsequent testing phase. This
additional stage is based on two processes:
Lau et al., 2011
Test
samples
Learned
Test Learned
structures sample structures
Model Principles
Backhaus et
al., 2011
Test
samples
Learned
patterns
Complete the test
sample with
activation based
on the correct
learned pattern
Output correlation
Diamond
Dog- ? - Letter
Test
Learned
patterns sample
Synaptic strength is reduced during sleep: Representative findings
General design: training  wake/sleep  testing
Hole - Sky
Objective:
Pairedassociates:
Background - II
Sleep improves performance: Representative findings
After sleep:
Sleep
Input correlation
Sleep extends pattern
differentiation
Sleep sharpens hippocampal inputto-output correlational differences
Conclusions
Synaptic deletion during sleep may play a computational role in
improving cognitive performance by differentiating and
unifying representations
References
A. Differentiation
Backhaus J, Born J, Hoeckesfeld R, Fokuhl S, Hohagen F, & Junghanns K (2007). Midlife decline in declarative memory consolidation is
correlated with a decline in slow wave sleep. Learning & Memory, 14, 336-341.
Ellenbogen JM, Hulbert JC, Stickgold R, Dinges DF, Thompson- Schill SL (2006). Interfering with theories of sleep and memory: sleep,
declarative memory, and associative interference. Current Biology, 16, 1290-1294.
Gluck MA, Myers CE (1993). Hippocampal mediation of stimulus representation: a computational theory. Hippocampus, 3, 491-516.
Lau H, Alger SE, Fishbein W (2011). Relational memory, a daytime naps facilitates abstraction of general concepts. PLoS One, 6. e27139.
Liu ZW, Faraguna U, Cirelli C, Tononi G, Gao XB (2010). Direct evidence for wake-related increases and sleep-related decreases in
synaptic strength in rodent cortex. Journal of Neuroscience, 30, 8671–8675.
Moustafa AA, Myers CE, Gluck MA (2009). A neurocomputational model of classical conditioning phenomena: a putative role for the
hippocampal region in associative learning. Brain Research, 1276, 180–195.
Plihal W, Born J (1997) Effects of early and late nocturnal sleep on declarative and procedural memory Journal of Cognitive Neuroscience,
9, 534–547.
Vyazovskiy VV, Cirelli C, Pfister-Genskow C, Faraguna U, Tononi G (2008). Molecular and electrophysiological evidence for net synaptic
potentiation in wake and depression in sleep. Nature Neuroscience, 11, 2, 200–208.
B. Unification
Differentiation and Unification processes. Each
row represents a different memory pattern
learned by the hippocampus during wake. Each
circle represents a unit (neuron). Red circles active units; White units – inactive.
Acknowledgements
Supported by Grant #7367437 for “Long-term Mobile
Monitoring and Analysis of Sleep-Cognition
Relationship” from the National Science Foundation's
Smart Health and Wellbeing program to M.A.G.
Contact
==
1Center
Itamar Lerner, [email protected]
Mark Gluck, [email protected]