The (perhaps) causal brain : A comparison of attractor neural networks usingtemporally symmetric and antisymmetric synapticrules

Abstract: The associative memory of the brain is thought to be well modelled by attractorneural networks. A sort of artificial neural network that may store memories and has the ability to associate them with distorted input. The memories may be stored in the system by changing the connecting weights depending on the activity pattern in the network, a process known as synaptic plasticity. There are several different theories of the conditions required for the strength of synaptic connection increase or decrease and which one of these that is the most likely is still an open issue. Many recent studies of the associative memory have used a model that only take correlated activity between neurons into account (BCPNN), but there is some experimental support for another one in which the exact timing of pre- and postsynaptic activity plays a role (STDP). There is, however, no conclusive evidence for either one and this study will, therefore, investigate the differences in an attractor neural network model using the two different rules for synaptic plasticity.In this study two simple attractor neural networks with 64 neurons werecreated, each using either STDP or BCPNN as a model for synaptic plasticity. Forcing the system into several states corresponding to different memories the connecting weights between the neurons changed. By stimulating the network with partial memory patterns its ability to recall memories remain stable could be tested. In several aspects, the system using STDP was found to perform better than BCPNN, and it is possible to conclude that the former synaptic rule was the better choice in this specific case.To draw any conclusions regarding which of STDP or BCPNN is more prob- able as a model for the synaptic plasticity in the brain more detailed studies would have to be undertaken. Preferably utilising more advanced and biologi- cally realistic models.