Understanding how neural circuits give rise to sensory computation and, ultimately, perception, requires connecting biological features of neural circuits to abstract models of neural computation. In vison, a model of the visual receptive field (RF) describes how a neuron's responses are determined by the visual inputs it encounters. The visual RF can also provide a compact description of a neuron's function, revealing which features of the external environment that neuron is responsible for encoding. Complex RFs in the visual system are responsible for high-level computations like object recognition as well as mid-level features of visual representation like size selectivity and translation invariance. The complexity of many higher-order visual circuits has made it difficult to connect models of these representations to the biological circuits that they are supposed to represent. Neural circuits across sensory systems and species often rely on convergent computational and algorithmic strategies to encode features of the external environment. This project will leverage this fact to address the question of the biological basis of complex RF structure in a tractable context, the visual system of Drosophila. In this postdoctoral training fellowship, the applicant will use state-of-the-art imaging techniques and fluorescent reporters of neural activity to describe complex RF features in neurons of the Drosophila optic lobe which project to the central brain. These results will be used to generate hierarchical models of complex RFs that are grounded in biological circuits. !
In this postdoctoral fellowship, the trainee will study the way neurons in the brain represent complex features of the visual world. This research will help reveal how the brain functions and provide insight into disorders of the nervous system.
