Excitation and inhibition in recurrent networks mediate collision avoidance in Xenopus tadpoles.

Information processing in the vertebrate brain is thought to be mediated through distributed neural networks, but it is still unclear how sensory stimuli are encoded and detected by these networks, and what role synaptic inhibition plays in this process. Here we used a collision avoidance behavior in Xenopus tadpoles as a model for stimulus discrimination and recognition. We showed that the visual system of the tadpole is selective for behaviorally relevant looming stimuli, and that the detection of these stimuli first occurs in the optic tectum. By comparing visually guided behavior, optic nerve recordings, excitatory and inhibitory synaptic currents, and the spike output of tectal neurons, we showed that collision detection in the tadpole relies on the emergent properties of distributed recurrent networks within the tectum. We found that synaptic inhibition was temporally correlated with excitation, and did not actively sculpt stimulus selectivity, but rather it regulated the amount of integration between direct inputs from the retina and recurrent inputs from the tectum. Both pharmacological suppression and enhancement of synaptic inhibition disrupted emergent selectivity for looming stimuli. Taken together these findings suggested that, by regulating the amount of network activity, inhibition plays a critical role in maintaining selective sensitivity to behaviorally-relevant visual stimuli.