1. Memory and Consolidation Prof.dr. Jaap Murre University of Amsterdam University of Maastricht jaap@murre.com http://www.neuromod.org/courses/cb2004

2. Overview Brief review of neuroanatomy of memory Outline of the TraceLink model Some simulation results of neural network model, focussing on retrograde amnesia Memory Chain Model –Forgetting –Amnesia

3. The Amnesia Paradox Recent items are remembered best But they are the first to be lost with (retrograde) amnesia

4. The Daily News Memory Test at memory.uva.nl 1810 Dutch respondents

5. Théodule Ribot (1886) Ribot’s Law: With memory loss, recent memories suffer more

6. x retrograde amnesia anterograde amnesia lesion presentpast Normal forgetting Ribot Gradient

7. Example: Patient data Kopelman (1989) News events test

8. Neuroanatomy of amnesia Hippocampus Adjacent areas such as entorhinal cortex and parahippocampal cortex Basal forebrain nuclei Diencephalon

9. The position of the hippocampus in the brain

10. Hippocampal connections

11. Hippocampus has an excellent overview of the entire cortex

12. The TraceLink Model A model of memory consolidation and amnesia

13. Trace-Link model: structure

14. System 1: Trace system Function: Substrate for bulk storage of memories, ‘association machine’ Corresponds roughly to neocortex

15. System 2: Link system Function: Initial ‘scaffold’ for episodes Corresponds roughly to hippocampus and certain temporal and perhaps frontal areas

16. System 3: Modulatory system Function: Control of plasticity Involves at least parts of the hippocampus, amygdala, fornix, and certain nuclei in the basal forebrain and in the brain stem

17. Stages in episodic learning

18. Dreaming and consolidation of memory Theory by Francis Crick and Graeme Mitchison (1983) Main problem: Overloading of memory Solution: Reverse learning leads to removal of ‘obsessions’ “We dream in order to forget”

19. Dreaming and memory consolidation When should this reverse learning take place? During REM sleep –Normal input is deactivated –Semi-random activations from the brain stem –REM sleep may have lively hallucinations

20. Consolidation may also strengthen memory This may occur during deep sleep (as opposed to REM sleep) Both hypothetical processes may work together to achieve an increase in the clarity of representations in the cortex

21. Experiment by Robert Stickgold Difficult visual discrimination problem Several hours of practice One group goes home Other group stays in the lab and skips a night of sleep

22. Improvement without further training due to sleep Normal sleep Skipped first night sleep

23. Relevant animal data by Matt Wilson and Bruce McNaughton (1994) 120 neurons in rat hippocampus PRE: Slow-wave sleep before being in the experimental environment (cage) RUN: During experimental environment POST: Slow-wave sleep after having been in the experimental environment

24. Wilson en McNaughton Data PRE: Slow-wave sleep before being in the experimental environment (cage) RUN: During experimental environment POST: Slow-wave sleep after having been in the experimental environment

25. Some important characteristics of amnesia Anterograde amnesia (AA) –Implicit memory preserved Retrograde amnesia (RA) –Ribot gradients Pattern of correlations between AA and RA –No perfect correlation between AA and RA

26. x retrograde amnesia anterograde amnesia lesionpresentpast Amnesia patient Normal forgetting

27. Example of patient data Kopelman (1989) News events test

28. Retrograde amnesia Primary cause: loss of links Ribot gradients Shrinkage

29. Anterograde amnesia Primary cause: loss of modulatory system Secondary cause: loss of links Preserved implicit memory

30. Semantic dementia The term was adopted recently to describe a new form of dementia, notably by Julie Snowden et al. (1989, 1994) and by John Hodges et al. (1992, 1994) Semantic dementia is almost a mirror- image of amnesia

31. Neuropsychology of semantic dementia Progressive loss of semantic knowledge Word-finding problems Comprehension difficulties No problems with new learning Lesions mainly located in the infero-lateral temporal cortex but (early in the disease) with sparing of the hippocampus

32. Severe loss of trace connections Stage-2 learning proceeds as normal Stage 3 learning strongly impaired Non-rehearsed memories will be lost No consolidation in semantic dementia

33. Semantic dementia in TraceLink Primary cause: loss of trace-trace connections Stage-3 (and 4) memories cannot be formed: no consolidation The preservation of new memories will be dependent on constant rehearsal

34. Connectionist implementation of the TraceLink model With Martijn Meeter

35. Some details of the model 42 link nodes, 200 trace nodes for each pattern –7 nodes are active in the link system –10 nodes in the trace system Trace system has lower learning rate that the link system

36. How the simulations work: One simulated ‘day’ A new pattern is activated The pattern is learned Because of low learning rate, the pattern is not well encoded at first in the trace system A period of ‘simulated dreaming’ follows –Nodes are activated randomly by the model –This random activity causes recall of a pattern –A recalled pattern is than learned extra

37. (Patient data) Kopelman (1989) News events test

38. A simulation with TraceLink

39. Frequency of consolidation of patterns over time

40. Strongly and weakly encoded patterns Mixture of weak, middle and strong patterns Strong patterns had a higher learning parameter (cf. longer learning time)

42. Transient Global Amnesia (TGA) (Witnessed onset) of severe anterograde and retrograde amnesia Resolves within 24 hours Retrograde amnesia may have Ribot gradients Hippocampal area is most probably implicated

43. Transient Global Amnesia (TGA)

44. Other simulations Focal retrograde amnesia Implicit memory More subtle lesions (e.g., only within-link connections, cf. CA1 lesions) Semantic dementia Schizophrenia (memory effects in -) with an extended model (added parahippocampal layer)

45. Alternative Explanations ‘Memory Bump’ appears as reverse gradient Nadel and Moscovitz (1997): Trace Replication Theory (will be discussed next time by the students)

46. Sir Francis Galton (1879) Inspected a cue word, e.g., coffee until an event came to mind Later, he dated the events

47. Lifetime distributions The Galton-Crovitz method aims for a quasi-random sample of autobiographical memories Stratified through the use of keywords Technically speaking: The method measures a probability density function of memory age

48. Rubin, Wetzler & Nebes (1986) Found a reminiscence bump between 10 and 30 years, when older than 40 years.

49. Herinneringsbobbel (Rubin, et al., 1986)

50. Large-scale replication using the internet Website: http://memory.uva.nl Steve JanssenAntonio Chessa

51. Subjects 827 men and 760 women The Netherlands M age = 39.89 years, SD age = 13.51 Six age groups between 10 and 70 year No financial reward

54. Memory age pdf Encoding Subject age

55. Memory age pdf Encoding

56. Encoding functions retains a contain shape over all age groups as was expected

57. Encoding combined over subject age classes N = 16955

58. When comparing across country and gender, we have: - Woman peak earlier - Americans peak earlier (found in nearly all age groups)

59. Memory Chain Model Model of learning, forgetting, retrograde amnesia

60. Three stages of learning and memory Encoding: formation of the memory, after a certain amount of learning time Storage: transformation of the memory, under the influence of rehearsal and consolidation Retrieval: search for the memory, based on a retrieval cue We assume that the contribution of the three stages is independent and multiplicative

61. Chain of memory ‘stores’ External Information Sensory Stores Working Memory Link System Trace System Long-term Memory Short-term Memory Loss from sensory store Loss from working memory Decay and interference

62. Chain of memory ‘stores’ External Information Store s 1 Store s 2 Store s S-1 Store s S Long-term Memory Short-term Memory Loss of intensity Sensory Memory

63. General principles of the multi- store model Part of the information is passed to the next store before it decays completely Subsequent stores hold information for longer time periods: slower decay rates in ‘higher’ stores

64. Item representation Items are represented as ‘copies’ or ‘critical features’, each of which suffices for recall Finding these ‘copies’ during recall is an inherently stochastic process

65. Neural network interpretation Jo Brand

66. Learning and forgetting as a stochastic process: 1-store example A recall cue (e.g., a face) may access different aspects of a stored memory If a point is found in the neural cue area, the correct response (e.g., the name) can be given Learning ForgettingSuccessful Recall Unsuccessful Recall

67. Performance determined by a single parameter: intensity Intensity is the expected number of copies found within the searched region Cf. the expected number of trees in a wood within any 5x5 m region We use the mathematical framework of point processes

68. General framework: encoding, storage, and retrieval (q = 1 in the remainder of this talk)

69. The contributions of individual stores can simply be added

70. Forgetting

71. One-store case: forgetting Assumption In all stores, we have an exponential decline of intensity with time t   is the intensity immediately after learning a 1 is the decline parameter

72. Formation and decline Longer learning times will lead to higher intensity Decline is caused by –interference from other items (not yet modeled) –displacement in some ‘buffer’ –loss of effectiveness of the search cue –neural ‘noise’ and competition

73. The shape of forgetting Forgetting in the one-store case:

74. Some properties of the forgetting curve Probability of recall always stays between 0 and 1 Forgetting is not necessarily greatest after learning: We predict a flex point when the initial recall is at least

75. Example: Single-store model fitted to short-term forgetting data R 2 = 0,985

76. Amnesia Retrograde amnesia

77. Assumption: Hippocampus (link system) = store 1 Neocortex (trace system) = store 2

78. x retrograde amnesia anterograde amnesia lesionpresentpast Amnesia patient Normal forgetting

79. Amnesia in the two-store model

80. Amnesia: animal data Retrograde amnesia

81. Cho & Kesner (1996). (mice) R 2 =0.96

82. Summary of animal data

83. Cortical amnesia Frankland, O’Brien, Ohno, Kirkwood, & Silva, (Nature, 2001). Data provided by Paul Frankland

84. Frankland et al. (2001) study  -CaMKB-dependent plasticity (in neocortex) switched off in knock-out mice No LTP measurable in neocortex but LTP in hippocampus was largely normal Forgetting curves with different levels of initial learning were measured A learning curve was measured Assumption: use r 1[2] (t) for knock-out mice

85. Forgetting after 3 shocks, using three parameters

86. Using the same three parameters and a massed-learning correction.

87. Controls receive 1 shock, experimental animals 3 shocks (no new free parameters).

88. Repeated learning for experimental animals (no new free parameters)

89. Summary of ‘cortical amnesia’. Using only 4 parameters for all curves (R 2 = 0.976).

90. Amnesia: human data Retrograde amnesia

91. Remarks on the human data Fitting procedure identical to animal data Data are very noisy Basic fits: a 2 = 0, full lesion assumed. This leaves three parameters.

92. Kopelman (1989). News events test. Korsakoff (left), Alzheimer (right, fitted with lower  2 ). R 2 =0.951.

93. Wiig, Cooper & Bear (1996). (rats) R 2 =0.28

94. Problem with nearly all human data Straight fits of forgetting curves and Ribot gradients are nonsense Tests items for remote periods are easier Typically: curves are flat around 80-85% for control subjects Reason: maximizes chances of detecting Ribot effects Disadvantage: shape of the curves is useless for quantitative analysis

95. Relative retrograde gradient: a relative measure of memory loss

96. Wiig, Cooper & Bear (1996). (rats) with rr-gradient: R 2 =0.84

97. Albert et al. (1979). Naming of famous faces. Korsakoff patients. R 2 = 0.977 and R 2 rr =0.978.

98. In progress with Memory Chain Model Fits to patient data: Huntington, Alzheimer, Korsakoff, TGA, focal lesions, ECT, etc. Data collection with four new tests of retrograde amnesia (in Dutch) developed in my group Memory Chain Model helps with item selection and interpretation of results (clinical application: diagnosis)

99. Summary of the human data This work is still in progress About 25 human data sets have been fitted rr-gradient allows initial quantitative analysis Human data and animal give the same overall picture

100. Concluding remarks Consolidation is still a hotly debated issue Modeling can help to elucidate the various viewpoints Models at various levels of detail can be developed: –Connectionist models (e.g., TraceLink) –Mathematical models (e.g., Memory Chain Model)

101. More information at http://www.neuroMod.org/ courses/cb2004 E-mail: jaap@murre.com