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HippocampusMedial Temporal Cortex
Itamar Lerner & Mark A. Gluck1Center for Molecular & Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
Sleep Mediation of Episodic Memory and Associative Learning II:Sleep Mediation of Episodic Memory and Associative Learning II:A Potential Computational SynthesisA Potential Computational Synthesis
Background - I
Sleep improves performance: Representative findings
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.
Introduction
Conclusions
Acknowledgements
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.
References
Per
form
ance
I. Episodic memory: Paired associates learning
Dog - Pianist
Hole - Sky
Memorizing
Diamond - Letter
Testing
II. Gist extraction: Learning meaning of Chinese characters:
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>eHierarchies with 2° separation: b>e
III. Rule extrapolation: Learning Implicit hierarchy between stimuli
General design: training wake/sleep testing
Background - II
Synaptic strength is reduced during sleep: Representative findings
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)
Model Principles
• 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).
Recall performance increases due to SWS between training and testing
Paired-associates task-design. Observation of pairs to be memorized is followed by 12hours of wake or sleep, after which cued recall is tested.
A shared pattern in Chinese characters is recognized better after sleep
Hierarchy rule is more easily recognized after sleep compared to wake
al., 2009) we assert that storing episodic memories, extracting gist information, or extrapolating a classification rule, all crucially depend on gradual learning of stimulus-stimulus associations in the hippocampus during wake. Only after learning these statistical regularities, can the system (Medial Temporal Cortex and Striatum) process appropriate responses.
Parsimonious representations facilitate cognitive performance
Before sleep: After sleep:
Paired-associates:
Gist extraction:
Complete the test sample with activation based on the correct learned pattern
Which of the two test samples fit better to the learned patterns?
Learnedpatterns
Testsample
Learnedpatterns
Testsample
Learnedpatterns Test
samples
Learnedpatterns Test
samples
Objective:
Rule learning:
Learnedstructures
Testsample
Learnedstructures
Testsample
To which of the two learned structures does the test sample fit?
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.
A. Differentiation
B. Unification
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.
Synaptic deletion during sleep may play a computational role in improving cognitive performance by differentiating and unifying representations
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:
1. Based on our previous NSF-supported modeling (Gluck and Myers, 1993; Moustafa et
Ellenbogen et al., 2006
Plihal & Born, 1997
Lau et al., 2011
Backhaus et al., 2011
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)
Sleep
Gradual learning during wake
Sleep extends pattern differentiation
Sleep sharpens hippocampal input-to-output correlational differences
After sleep-dependent unification and differentiation, each objective is more readily accessed:
Input correlation
Out
put
corr
elat
ion
Contact
==
Itamar Lerner, [email protected] Gluck, [email protected]
