Neil Burgess' talk discussed using computational modelling in neuroscience and education. Models can help characterize how learning changes the brain by developing a model that displays the same behaviour as observed in the brain or behaviourally. This provides a theory for how the process works. Models can also explore constraints on learning and change from factors like environment, genes, and the developing brain. Models should be constrained by neuroscience findings on mechanisms of synaptic change and how brain regions interact to produce behaviour. Finding an appropriate level of abstraction in models may bridge the gap between neuroscience and psychology.

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