Interpretation of neuronal activity in neural networks

In this paper we will compare the performance of two techniques (the Maximum Likelihood Estimation (MLJZ) and the Population Vector (PV)) for estimating the interpretation of neuronal activity in a population of neurons. Although such a comparison has been made before, so far only homogeneous distributions of receptive fields have been investigated. Since the performance of both methods depends on the distribution of the receptive fields we have tested the performance for homogeneous and inhomogeneous distributions. The results demonstrate that in general the ML& method outperforms the Population Vector. However, the MLE method depends heavily on the shape of the receptive field properties of the neurons, which is not the case for the PV method. Moreover, the MLE method may give rise to artefactual results for inhomogeneous distributions of receptive fields. For the PV method the shape of the receptive field is not as important. Moreover, for the Population Vector the optimal width of the receptive field remains more or less constant when the decrease in density is small relative to the optimal width. In this case the information decreases proportionally with the density of receptive fields. J&~wcw&: Neural network; Population vector; Maximum likelihood estimation; Receptive field