Abstract
Power laws describe brain functions at many levels (from biophysics to psychophysics). It is therefore possible that they are generated by similar underlying mechanisms. Previously, the response properties of a collision-sensitive neuron were reproduced by a model which used a power law for scaling its inhibitory input. A common characteristic of such neurons is that they integrate information across a large part of the visual field. Here we present a biophysically plausible model of collision-sensitive neurons with η-like response properties, in which we assume that each information channel is noisy and has a response threshold. Then, an approximative power law is obtained as a result of pooling these channels. We show that with this mechanism one can successfully predict many response characteristics of the Lobula Giant Movement Detector Neuron (LGMD). Moreover, the results depend critically on noise in the inhibitory pathway, but they are fairly robust against noise in the excitatory pathway.
Highlights
Noise is usually unwanted in signals, because it may introduce errors in transmitted messages
Many different animals try to escape from collision threats, because it is very possible that the approaching object is a predator
The neurons of different animals which selectively respond to approaching objects have very similar properties
Summary
Noise is usually unwanted in signals, because it may introduce errors in transmitted messages. In the nervous system, noise is ubiquitous and ineluctable. For instance, photons arrive at the retina according to a Poisson process [1], giving rise to photon noise. The chemical signal is amplified and transduced into an electrical signal. The latter process introduces transduction noise [2]. Each subsequent stage of (visual) information processing adds further cellular, electrical, or synaptic noise [3, 4]. The reliability at which organisms perform at the behavioural level evidences that the nervous system is designed in a way to handle noise well [5] while reducing power demand [6]
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