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. How does consolidation work in a TraceLink? Simulated slow-wave sleep –Model runs freely without input –Typically retrieves a random pattern –Trace connections are strengthened after random retrieval This process is repeated for the duration of the ‘dreaming period’ This implementation is based on the ‘sleep- consolidation hypothesis’

12. 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”

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

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

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

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

17. 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

18. Application Spontaneous recovery Guided recovery: rehabilitation from brain damage

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

21. 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?

22. 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

23. 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

24. 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

25. Multi-level modeling in cognitive neuroscience

26. Additional levels can be considered as well 2.a Neural networks: between high-level connectionist and mathematical 4. Single-neuron: below low-level connectionist

27. Multi-level modeling in the PIONIER group Modeling is mainly concentrated at levels 2 and 3 We expect more research efforts at level 1 Some work is done at level 4

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

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

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

31. Illustration of different levels of modeling in our group

32. 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)

33. 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

34. 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

35. Example of a level 3 model Synfire chain model

36. 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?

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

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

39. 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

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

41. 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

42. 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 Learning ForgettingSuccessful Recall Unsuccessful Recall

43. 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

44. Hellyer (1962). Recall as a function of 1, 2, 4 and 8 presentations Two-store model with saturation. Parameters are  1 = 7.4, a 1 = 0.53,  2 = 0.26, a 2 = 0.31, r max = 85; R 2 =.986

45. 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

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

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

48. 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)

49. Obtaining data to model

50. 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

51. 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

52. 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)

53. 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

54. Research infrastructure and knowledge management

55. Infrastructure for research and knowledge management Simulation software Dissemination of results Preservation and exchange of knowledge within the group

57. Neurosimulation software developed by us: Walnut and Nutshell Aimed at users in cognitive neuroscience Greatly shortens development cycle of new models Useful to both naïve and expert users Exchange of paradigms and simulations across the internet via NNML Scriptable in VBScript, Python, etc.

58. Dissemination of results How to publish or obtain models? Geppetto project: ‘Bring models to life’ Database of –models –neurosimulators (modeling software) –data –researchers and laboratories

60. Dissemination of results (cont’d) Presentation of the PIONIER group’s activities neuromod.org (neuromod.uva.nl): research memory.uva.nl: general audience

63. Preservation and exchange of knowledge within the group Intranet for within-group cooperation and exchange Database management (with backups etc.) Documentation of procedures Version control system (great ‘Undo’) Issue and task management (e.g., bugs) HowTo texts

64. Concluding remarks

65. Modeling in a multi-discipline Our models incorporate data from: –Neuroanatomy and neurophysiology –Neurology and neuropsychology –Experimental psychology The ultimate aim is to integrate these various sources of data into a single framework that is implemented as a series of coordinated models

66. Steps towards the goal In the following two hours, we will present some of our progress made towards that goal