Brain Plasticity and the Stability of Cognition

1. Brain Plasticity and the Stability of Cognition Studies in Cognitive Neuroscience Jaap Murre University of Amsterdam

2. Overview • Background to two of our models • Principles of multi-level modeling • How our models are related • How we obtain our data • Research infrastructure and knowledge management

3. Background to two of our models • TraceLink model • Selfrepairing neural networks as a framework for recovery from brain damage

4. TraceLink model Connectionist model of memory loss and certain other memory disorders

5. TraceLink model: structure

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

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

8. Location of the hippocampus

9. 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

10. Stages in episodic learning

11. Sleep-consolidation hypothesis • Memories are reactivated during slow-wave sleep • This leads to a strengthening of their cortical basis • After many weeks, the memories become independent of the hippocampus • Unverified hypothesis: “Without such consolidation, memories remain dependent on the hippocampus”

12. Selfrepairing neural networks A framework for a theory of recovery from brain damage

13. Redundancy and repair • Redundancy by itself does not guarantee survival • Only a continuous repair strategy does • Example: safeguarding a rare manuscript

14. Redundancy and repair example • Lesion: Suppose there is a 50% loss rate

15. Redundancy and repair example • Repair: At the end of each month new copies are made of surviving information

16. This process has a long life-time • Monthly ‘lesion-repair’ continues for many months ... • ... until all information is lost at the end of one unfortunate month • Chances of this happening are very low • The expected life-time of the manuscript in this example is over 80 years

17. Application • Spontaneous recovery • Guided recovery: rehabilitation from brain damage

18. Studies in cognitive neurosciene Principles of multi-level modeling

19. From brain to behavior • Cognitive neuroscience, formerly called ‘Brain and Behavior’ • Question: How to bridge the gap between these two exceedingly complex objects of study? • Partial answer: Through the construction of models • But at what level should we model?

20. The problem • Even simple behavior involves dozens of neural processes and structures with hundreds of parameters in total • We are therefore forced to abstract from neural details • Abstractions are based on assumptions about their • characteristics • interdependence

21. Detail and abstraction • Verify assumptions with more detailed models • Unfortunately: these simulations are very time consuming • Therefore: show that they possess the essential characteristics that are assumed • Low-level models are mainly suitable for verifying predictions at the level for which they have been developed

22. Principles of multi-level modeling • We should model at several levels of abstraction • Models at consecutive levels should be coordinated • This is achieved by referring to the same concepts, processes, and structures • Multi-level modeling is akin to having road maps at different levels of resolution

23. Multi-level modeling in cognitive neuroscience

24. Level 1. Mathematical models • Abstraction and generalization of TraceLink model with point process based models • Investigation of possible neural basis of the REM model

25. Level 2. High-level computational models • TraceLink model • Selfrepair model • Hemineglect model

26. Level 3. Low-level computational models • Model of neural linking in the cerebral cortex • Hippocampus model • Parahippocampus model • Model of somato-sensory cortex

27. Illustration of different levels of modeling in our group

28. TraceLink as a starting point (level 2 model) • Direct applications • Retrograde amnesia (loss of existing memories) • Shape of the Ribot gradient (loss of recent memories) • Strongly versus weakly encoded patterns • Semantic dementia (loss of what things mean) • Inverse Ribot gradient (preservation of recent memories)

29. Extensions of TraceLink (level 2) • Schizophrenia • Memory impairment is central in the ‘core profile’ of schizophrenia • Categorization • How and when should new categories be formed

30. Detailing TraceLink (level 3) • Trace system • Model of the formation of synfire chains: long-range connections via a chain of neurons • Link system • Hippocampal model • Parahippocampal model • Modulatory system • Novelty-dependent plasticity

31. Example of a level 3 model Synfire chain model

32. Formation of long-range connections in the cortex • If two remote brain sites A and B must communicate via intermediary neurons, how is a communication path set up? • Can such a path develop with normal learning?

33. Based on the work of Abeles: so called synfire chains ... A • Reliable transmission • Increasing biological evidence • The development of synfire chains, however, has not been simulated in a satisfactory manner B Group 1 Group 2 Group 3

34. Simulations • We used a more biologically realistic model neuron (McGregor neuron) • Self-organization of cortical chains was observed

35. Main characteristics of the development of synfire chains • Chains develop with repeated stimulation of one or more groups • A chain grows out of a stimulated group • Early parts of a chain stabilize before late groups

36. Example of level 1 model Point process model of learning, forgetting, and retrograde amnesia (loss of existing memories)

37. Abstracting TraceLink (level 1) • Model formulated within the mathematical framework of point processes • Generalizes TraceLink’s two-store approach to multiple ‘stores’ • trace system • link system • working memory, short-term memory, etc. • A store corresponds to a neural process or structure

38. Learning and forgetting as a stochastic process • 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 Forgetting Successful Recall Unsuccessful Recall Learning

39. Some aspects of the point process model • Model of learning and forgetting • Clear relationship between recognition (d'), recall (p), and savings (Ebbinghaus’ Q) • Multi-trial learning and multi-trial savings • Massed versus spaced effects • Applied to retrograde amnesia (hippocampus is store 1, which is lesioned) • Applied to many learning and forgetting data

40. Hellyer (1962). Recall as a function of 1, 2, 4 and 8 presentations Two-store model with saturation. Parameters are m1= 7.4, a1= 0.53, m2= 0.26, a2= 0.31, rmax= 85; R2=.986

41. Retrograde amnesia (RA) • RA is loss of existing memories • In current RA tests, questions about remote time periods are often easier than of recent time periods • This makes them largely useless for modeling • Our model can offer a solution because it can cancel the variations in item difficulty

42. Albert et al. (1979), naming of famous faces

43. Example of multi-level approach The same concept at three different levels

44. Learning associations between aspects of an experience • Level 1. Increase of intensity through induction of ‘points’ (PPM model) • Level 2. Hebbian learning between neural groups or ‘nodes’ (TraceLink) • Level 3. Development of long-range cortical synfire chains (synfire chain model)

45. Obtaining data to model

46. Obtaining data to model • Literature search • Collaboration • Semantic dementia model: Cambridge group at Medical Research Council - Cognition and Brain Sciences Unit • Schizophrenia model: Washington Group at the National Institute of Mental Health • Selfrepair and rehabilitation: Dublin group at Trinity College

47. Obtaining data to model: quantitative neuroanatomy • Relatively little is known about mesoscopic aspects of the brain • In particular, we do not know how neurons are connected • We infer this mesoscopic level through mathematical modeling • These data are of particular relevance for models at levels 2 and 3

48. Obtaining data to model: retrograde amnesia (RA) • No RA tests in Dutch. Therefore: • Official translation of British test • Public events test • Novel aspect: using the internet to obtain data on long-term forgetting (Daily News Test)

49. Direct investigation of consolidation: sleep experiment • Consolidation lies at the heart of the PIONIER projects • Much circumstantial evidence for the existence of memory consolidation during sleep • No direct evidence • Therefore: investigate this ourselves • Also: makes integration of our group with the neurosciences more of a reality

50. Research infrastructure and knowledge management