Neural bases of honeybee visual information processing and locomotion control

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Photoreceptors represent light changes through their synaptic transmission. Postsynaptic interneurones then encode these messages into responses that represent similarities and differences of real-world objects consistently. When the world seems noisy (in dim light), it is best captured by summing up its events slowly, as this increases the reliability of vision. But when the world is unambiguous (in bright light), it is important to enhance its differences fast, as this best utilises the neurones' limited channel capacity. Thus, to maximise information flow, neural representations of the world should adapt to its changing statistics. Although photoreceptor synapses have been implied to follow these coding rules, the mechanisms underpinning their adaptive information processing remain a mystery. The aim of this P.D.-project is to quantify and model how quantal synaptic information transfer in honeybee photoreceptor synapses adapts to changing light conditions. By using electrophysiological approaches while monitoring behavior in novel virtual reality systems and mathematical modelling, the student will investigate how pre- and postsynaptic mechanisms are tuned together to maximise synaptic information transfer and how this information is represented and distributed for the bee visual perception. We anticipate that such knowledge would have direct impact in guiding the design of future neural implants, other biomimetic devises and robotic vision.