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

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Time (s)

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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

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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