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Computational neuroimaging: quantitative models of human visual neurons (pRF Models)
Start date: 01 Feb 2009, End date: 31 Jan 2013 PROJECT  FINISHED 

One of the most complex systems, often referred to as science’s last frontier, is the human brain. However, human neuroscience research is constrained; it has no or rarely access to invasive procedures that are widely used in animals. Invasive procedures allow measurements at much smaller scales, e.g. at level of individual neurons. Consequently, most knowledge of human neurons is extrapolated from animal experiments. Ultimately, at least some human and animal neuronal properties will differ, making human measurements at comparable scales essential. We propose to bridge this gap by coupling non-invasive human neuroimaging signals, measured at the millimetre scale, with neuronal properties, measured at the micron scale. We will use a new computational neuroimaging method, which measures human neuronal population properties that are close to those derived from invasive animal experiments (Dumoulin and Wandell, 2008). The utility of these methods extends from basic to applied neuroscience, as supported by initial observations in both rare, i.e. achiasma, and common disorders, i.e. macular degeneration a leading cause of visual impairment. Thus, this method has both fundamental and clinical applications. We will extend this method in three ways. First, we will extend this method from human population to single neuron estimates. We require that our estimates replicate well-established animal experiments and human behavioural data. Second, we will validate the measurements with predictions derived from an established theoretical framework. The ability to confirm basic observations is crucial for correct interpretations of potential differences. The current theoretical framework was established using artificial stimuli that are supposed to extrapolate to natural conditions. However, recent studies suggest that this extrapolation capability is limited. Third, we will extend this method to build more complex models of neuronal properties under natural viewing conditions.
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