Neuroscience, Computational Modelling and Education: Reflections on Neil Burgess’ talk Gert Westermann

Modelling already featured in Neil’s talk: x j x i w ij e.g. w ij -> w ij + ε x j x i w ij -> w ij + Δ w ij x j w j Different learning rules in hippocampus and striatum?

So what can modelling offer to neuroscience and education?

BEHAVIOUR Huge gap! Computational models

Models… … help us to understand how learning changes the brain (to characterize the process of change) Basic idea: We observe a process (e.g., brain-behaviour correlates), or more relevant here, a behavioural change We develop a computational model that displays the ‘same’ behaviour We know how the model works, and this becomes our theory of how the process works in real life But this is not always followed. /rIt/ write

… help us to understand how learning changes the brain

(to characterize the process of change)

Basic idea:

We observe a process (e.g., brain-behaviour correlates),

or more relevant here, a behavioural change

We develop a computational model that displays the ‘same’ behaviour

We know how the model works, and this becomes our theory of how the process works in real life

But this is not always followed.

Neural network (connectionist) models Added (important) benefit: Functionality of these models is inspired by how neurons work Although we should stay alert to the limits of this analogy.

Added (important) benefit:

Functionality of these models is inspired by how neurons work

Characterizing constraints on change Models as a tool to explore what affects change: Environment frequency of exposure order of exposure (age of acquisition) type of exposure (e.g., similarity between stimuli)

Models as a tool to explore what affects change:

Environment

frequency of exposure

order of exposure (age of acquisition)

type of exposure (e.g., similarity between stimuli)

Characterizing constraints on change Genes/internal constraints Structure/resources of the learning system (critical periods, developmental disorders, speed-accuracy trade-off in learning) ε = 0.1

Genes/internal constraints

Structure/resources of the learning system

(critical periods, developmental disorders, speed-accuracy

trade-off in learning)

Characterizing constraints on change Links between brain and cognitive development Effect of environmental exposure on development of functional structures Effect of the integration of subsystems on behaviour Maturation and experience-dependent plasticity

Links between brain and cognitive development

Effect of environmental exposure on development of

functional structures

Effect of the integration of subsystems on behaviour

Maturation and experience-dependent plasticity

These aspects of models should be constrained by neuroscience: Mechanisms of synaptic change Interplay of functional brain regions and give rise to relevant behaviour. Converging evidence

These aspects of models should be constrained by neuroscience:

Mechanisms of synaptic change

Interplay of functional brain regions

and give rise to relevant behaviour.

Converging evidence

Bridging the gap… Models can be built at different levels of abstraction. Is there a level that is acceptable both to neuroscientists and psychologists? I think: yes, if we constantly remind ourselves what a model is for.

Models can be built at different levels of abstraction.

Is there a level that is acceptable both to neuroscientists and psychologists?

I think: yes, if we constantly remind ourselves what a model is for.