Place field detection using grid-based clustering in a large-scale computational model of the rat dentate gyrus

Decoding Position to Analyze Spatial Information Encoding in a Large-Scale Neuronal Network Model of Rat Dentate Gyrus

Spatial information is encoded by the hippocampus, and the factors that contribute to the amount of information that can be encoded and the transformation of spatial information through the trisynaptic circuit remain an important issue. A large-scale neuronal network model of the rat entorhinal-dentate system was developed with multicompartmental representations of the neurons within the dentate gyrus. Spatial information was introduced to the network via grid cell activity, and the spatial information encoding capabilities of the network were assessed using a recursive decoding algorithm to estimate the position of a virtual rat using the dentate activity. To obtain a measure for the information that the network could convey, decoding error was calculated for different decoding population sizes. Decoding error decreased exponentially as a function of population size. Therefore, the time constant and the asymptote of the error curve could be used as metrics to compare the changes in encoding performance. In conjunction with the large-scale model, this paradigm can be used to characterize how neural properties, network composition, and the interactions between different subfields affect spatial information encoding.