Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector
Figure 5
Mean population responses of the different layers of our model to a uniformly expanding or receding stimulus.
The right most panel depicts the modelled anatomical organization of the pathway to the LGMD (A–E) where (A) is the photoreceptor layer, (B) the lamina (the centre/surround architecture), (C) the medulla (containing the on-off neurons), (D) the neurons connecting to the excitatory pathway of the LGMD, and (E) the LGMD/DCMD output. Left panel: Average population response for each of the layers of our model (depicted in the right most diagram) to an object that is uniformly increasing in size (10 repetitions). The curves in (E) show the different responses predicted by our model (solid line) and generated by the model proposed by Gabbiani et al. (1999) (dashed grey line) to the same stimulus. Right panel: Population responses for each of the layers of our model to receding stimuli (32 repetitions). The gain of the excitatory input to the LGMD was fixed to 0.2 while 4 different gains of the inhibition were tested (0.02, 0.01, 0.005, and 0.001). (E) shows the 4 predictions for the responses of the LGMD depending on the inhibitory weight to a receding stimulus. The input data to the LGMD was fitted with the instantaneous angular size of the object in (C) and (D) (red dashed line). The half-length of the objects for both linearly increasing and receding stimulation experiments was fixed to 30cm. In the case of the receding experiment, the simulated object was moving away from the camera at 10m/s.