Computational modelling of transformation invariant representations and information maintenance in spiking neural networks with ranges of axonal condu

The application of computational visual neuroscience to the modelling of higher-level brain function and practical robotics has been limited by the absence of stereoscopic primate models of high-level, three-dimensional vision. It is only recently that neurophysiological characterisations of three-dimensional object shape representation in the macaque inferior temporal cortex (IT) have emerged, providing results for computational models to replicate. The role of IT's visual representation in the fundamental functions of visual attention (Chelazzi et al., 1993), object recognition (Hung et al., 2005) and object interactions (Gallese et al., 1994), makes its replication as a modular interface between reality (both physical and virtual) and higher-level modelling, of significant interest to artificial intelligence and of critical importance to future computational neuroscience research.
This research proposal plans to replicate the IT representation of three-dimensional object shape found by Yamane et al. (2008), within the hierarchical VisNet model of primate vision. This progresses the research of Eguchi et al. (2015) of the OCTNAI in replicating characterisations of two-dimensional object shape representation in V4 and posterior IT (TEO), within the VisNet model. The adaptive stimuli technique and analysis provided by Yamane et al. will allow for a direct comparison of the model with neurophysiological results. This work will also provide the basis for modelling recent characterisations of more advanced representations of object shape in IT. The functionality of the initial model's representations for higher-level processing will be tested by modelling the theorised role of the anterior intraparietal cortex (AIP) in mapping IT shape representation to motor commands for grasping objects. This will incorporate the research of the OCTNAI in modelling head-centred (Mender and Stringer, 2014) and hand-centred (Galeazzi et al., 2015) egocentric reference frames with the aim of producing a practical model of visually guided reaching and grasping.
As the central area of investigation, the proposal begins by outlining characterisations of three-dimensional object shape representation in IT and describes hypotheses of how such representations may self-organise within a stereoscopic version of VisNet. This is followed by an outline of my current internship research, which has aimed to produce a biologically accurate model of stereoscopic disparity in V1, to act as a model of binocular combination for input into higher-level VisNet experiments. In particular, it focuses on the extensions I have made to the Topographica model of stereoscopic vision in V1, in order to improve the biological accuracy of its representation of disparity in line with recent neurophysiological results. Characterisations of disparity tuning in V2 and V4 are also discussed, which will act as intermediate benchmarks for the model. The proposal specifies experiments to test the theorised role of AIP in reaching and grasping. Finally, details of my suitability for undertaking this research are outlined.