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Brain Plasticity and the Stability of Cognition
Studies in Cognitive Neuroscience
Jaap Murre
University of Amsterdam
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
Background to two of our models
• TraceLink model
• Selfrepairing neural networks as a framework for recovery from brain damage
TraceLink model
Connectionist model of memory loss and certain other memory disorders
TraceLink model: structure
System 1: Trace system
• Function: Substrate for bulk storage of memories, ‘association machine’
• Corresponds roughly to neocortex
System 2: Link system
• Function: Initial ‘scaffold’ for episodes
• Corresponds roughly to hippocampus and certain temporal and perhaps frontal areas
Location of the hippocampus
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
Stages in episodic learning
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”
Selfrepairing neural networks
A framework for a theory of recovery from brain damage
Redundancy and repair
• Redundancy by itself does not guarantee survival
• Only a continuous repair strategy does
• Example: safeguarding a rare manuscript
Redundancy and repair example
• Lesion: Suppose there is a 50% loss rate
Redundancy and repair example
• Repair: At the end of each month new copies are made of surviving information
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
Application
• Spontaneous recovery
• Guided recovery: rehabilitation from brain damage
Studies in cognitive neurosciene
Principles of multi-level modeling
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?
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
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
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
Multi-level modeling in cognitive neuroscienceLevel Brain Behavior1. Mathemati-cal
AbstractedNeuralSystems
Quantitative
2. High-levelConnectionist
NeuralSystems
Qualitative
3. Low-levelConnectionist
Details of oneor two systems
UnderlyingPrinciples
Level 1. Mathematical models
• Abstraction and generalization of TraceLink model with point process based models
• Investigation of possible neural basis of the REM model
Level 2. High-level computational models• TraceLink model
• Selfrepair model
• Hemineglect model
Level 3. Low-level computational models• Model of neural linking in the cerebral
cortex
• Hippocampus model
• Parahippocampus model
• Model of somato-sensory cortex
Illustration of different levels of modeling in our group
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)
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
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
Example of a level 3 model
Synfire chain model
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?
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 1 Group 2 Group 3
A B
Simulations
• We used a more biologically realistic model neuron (McGregor neuron)
• Self-organization of cortical chains was observed
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
Example of level 1 model
Point process model of learning, forgetting, and retrograde amnesia
(loss of existing memories)
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
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
LearningForgettingSuccessfulRecallUnsuccessfulRecall
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
Hellyer (1962). Recall as a function of 1, 2, 4 and 8 presentations
0
0.2
0.4
0.6
0.8
1
0 10 20 30
Time (s)
Re
ca
ll p
rob
ab
ility
Two-store model with saturation. Parameters are1= 7.4, a1= 0.53, 2= 0.26, a2= 0.31, rmax= 85; R2=.986
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
Albert et al. (1979), naming of famous faces
a.
0
0.5
1
70s 60s 50s 40s 30s
Controls (N=15)Korsakoff's (N=11)Series3Series4
Example of multi-level approach
The same concept at three different levels
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)
Obtaining data to model
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
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
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)
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
Research infrastructure and knowledge management
Infrastructure for research and knowledge management
• Simulation software
• Dissemination of results
• Preservation and exchange of knowledge within the group
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.
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
Dissemination of results (cont’d)
• Presentation of the PIONIER group’s activities
• neuromod.org (neuromod.uva.nl): research
• memory.uva.nl: general audience
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
Concluding remarks
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
Steps towards the goal
• In the following two hours, we will present some of our progress made towards that goal