Role of Context in Episodic Memory : A Bayesian-Hebbian Neural Network Model of Episodic Recall

Sammanfattning: Episodic memory forms a fundamental aspect of human memory that accounts for the storage of events as well as the spatio-temporal relations between events during a lifetime. These spatio-temporal relations in which episodes are embedded can be understood as their contexts. Contexts play a crucial role in episodic memory retrieval. Despite this, little work has been done in the computational neuroscience literature on trying to investigate this relationship further. These interactions can be modelled with attractor neural networks such as the Bayesian Confidence Propagation Neural Network (BCPNN). In this project, the interaction between contextual aspects and memory items are studied by developing an abstract computational model of episodic memory retrieval. The effect of increasing the number of items associated with a particular context on the overall recall performance is examined. Finally, the role of synaptic plasticity modulation of certain item-context associations on recall is also analysed. It is found that an inverse relationship exists between the number of items associated with a context and their subsequent recall rates, i.e. as the number of items associated with an episodic context increase, the recall rates of the corresponding items decrease. Furthermore, it is found that the item-context pairs for which the synaptic plasticity is modulated during learning, have a significantly higher recall rate than the remaining unmodulated associations.