We seek to understand how populations of neurons collectively process sensory input, perform computations and control behaviour. In particular, we are interested in how internal states and processes influence neural activity and perceptual decisions. To this end, we develop statistical models and machine learning algorithms for neural data analysis, and collaborate with experimental laboratories performing measurements of neural activity and behaviour.
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One key challenge in neuroscience is to elucidate mechanistic principles of how the brain integrates sensory information from its environment to generate behavior. At present, experimental methods to directly monitor sensory-evoked streams of excitation throughout the brain, at cellular and millisecond resolution are lacking. To overcome these limitations, the ‘Computational Neural Anatomy Group’ seeks to develop an alternative reverse engineering approach. The novel approach comprises reconstructing the detailed 3D structure of neural circuits, quantifying local and long-range synaptic connectivity and simulating sensory-evoked signal flow within the resultant anatomically realistic network models. [more]