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

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

a.

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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