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

Abstract

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.

Figure 1.

Rate maps generated by simulated granule cells. The rate maps were smoothed using adaptive binning. The red areas represent areas of higher firing rate while blue areas represent lower firing rate. Multiple place fields can be seen, and they are arranged irregularly throughout the environment.

Figure 2.

Estimated position based on different population sizes of neurons over a 20 second period. Each subplot shows the actual x and y coordinates of the virtual rat (black). The estimated position is overlaid in red. As the number of neurons used during decoding increases, the estimate becomes more accurate. A lag can be seen between the actual position and estimated position. This lag is 356 ms.

Figure 3.

Mean error plotted against population size. As population size increases, error decreases exponentially. An asymptotic value of 1.5 predicts the minimum error that can be achieved by the network at a population size of infinity.