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CEN launch, Gert Westermann

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  • 1. Neuroscience, Computational Modelling and Education: Reflections on Neil Burgess’ talk Gert Westermann
  • 2. 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?
  • 3. So what can modelling offer to neuroscience and education?
  • 4. BEHAVIOUR Huge gap! Computational models
  • 5. 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
  • 6. 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.
  • 7. 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)
  • 8. 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
  • 9. 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
  • 10.
    • 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
  • 11. 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.