My thesis demonstrates how cognitive models can learn associative memories to permanently improve their performance from experience. Neurally, this was implemented using a combination of the supervised Prescribed Error Sensitivity and unsupervised Vector Oja learning rules can be used to learn associative memories. Associative memory learning is then applied to provide explanations of two behaving practice-based cognitive phenomena via modeling with spiking neurons.
First, the standard progression of cognitive addition strategies from counting to memorization, as occurs in children, is modelled as a transfer of skills. Initially, addition by counting is performed in the slow basal ganglia based system, before being overtaken by a rapid cortical associative memory as a type of pre-frontal, cortical consolidation.
Second, a word-pair recognition task, where two distinct types of word-pairs are memorized, is modelled. The Voja learning rule is modified to match temporal lobe magnetoencephalography (MEG) data generated by each word-pair type observed during the task. This empirically grounds the associative memory model, which has not been possible using other cognitive modeling paradigms.
The distinct implementation of Voja for each area, pre-frontal and temporal, demonstrates the different roles that the areas perform during learning. The thesis concludes by proposing psychological experiments to empirically validate these different roles.
I previously worked at the CNRG during the final co-op term of my undergraduate degree translating Dan Rasmussen's HRL code from Nengo 1.4 to Nengo 2.0. I also developed a proof of concept GUI for visualising the data I was getting from the model, which then evolved (with the hard work of my lab-mates) into the current Nengo GUI.