Abstract

Visual neurons specialized in tracking objects on a collision course are often finely tuned to their target stimuli as this is critical for survival. The presynaptic neural networks converging on these neurons and their role in tuning them remains poorly understood. We took advantage of well-known characteristics of one such neuron to investigate the properties of its presynaptic input network. We find a structure more complex than hitherto realized. In addition to dynamic lateral inhibition used to filter out background motion, presynaptic circuits include normalizing inhibition and short-range lateral excitatory interactions mediated by muscarinic acetylcholine receptors. These interactions preferentially boost responses to coherently expanding visual stimuli generated by colliding objects, as opposed to spatially incoherent controls, helping implement object segmentation. Hence, in addition to active dendritic conductances within collision detecting neurons, multiple layers of both inhibitory and excitatory presynaptic connections are needed to finely tune neural circuits for collision detection.