Computation of Object Approach by a Biophysical Model of a Wide-Field Visual Neuron: Dynamics, Peaks, and Fits

TL;DR

This paper introduces the PSI-model, a biophysical model of the LGMD neuron in locusts, which improves upon the ETA-function by addressing its limitations and accurately fitting experimental data on object approach responses.

Contribution

The PSI-model explicitly links angular size and velocity to biophysical mechanisms, avoiding previous issues and providing a better fit to experimental data than the ETA-function.

Findings

PSI-model explains activity peaks after time-to-contact.

PSI-model fits experimental data as well as ETA-function.

Divisive inhibition implements multiplicative effects in the model.

Abstract

Many species show avoidance reactions in response to looming object approaches. In locusts, the corresponding escape behavior correlates with the activity of the lobula giant movement detector (LGMD) neuron. During an object approach, its firing rate was reported to gradually increase until a peak is reached, and then it declines quickly. The ETA-function predicts that the LGMD activity is a product between an exponential function of angular size and angular velocity, and that peak activity is reached before time-to-contact (ttc). The ETA-function has become the prevailing LGMD model because it reproduces many experimental observations, and even experimental evidence for the multiplicative operation was reported. Several inconsistencies remain unresolved, though. Here we address these issues with a new model (PSI-model), which explicitly connects angular size and angular velocity to biophysical quantities. The PSI-model avoids biophysical problems associated with implementing exp(), implements the multiplicative operation of ETA via divisive inhibition, and explains why activity peaks could occur after ttc. It consistently predicts response features of the LGMD, and provides excellent fits to published experimental data, with goodness of fit measures comparable to corresponding fits with the ETA-function.