Salience invariance with divisive normalization in higher-order insect neurons

We present a biologically inspired model for estimating the position of a moving target that is invariant to the target's contrast. Our model produces a monotonic relationship between position and output activity using a divisive normalization between the `receptive fields' of two overlapping, wide-field, small-target motion detector (STMD) neurons. These visual neurons found in flying insects, likely underlie the impressive ability to pursue prey within cluttered environments. Individual STMD responses confound the properties of target contrast, size, velocity and position. Inspired by results from STMD recordings we developed a model using a division operation to overcome the inherent positional ambiguities of integrative neurons. We used genetic algorithms to determine the plausibility of such an operation arising and existing over multiple generations. This method allows the lost information to be recovered without needing additional neuronal pathways.