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Ennio Mingolla, N Andrew Browning, Stephen Grossberg; Neural dynamics of visually-based object segmentation and navigation in complex environments. Journal of Vision 2008;8(6):1154. doi: https://doi.org/10.1167/8.6.1154.
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© ARVO (1962-2015); The Authors (2016-present)
Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. A dynamical neural model proposes how primates use motion information to segment objects and determine heading. These competences reflect complementary properties of MT- / MSTv and MT+ / MSTd pathways. The model clarifies the functions of magnocellular cells in primate retina, V1, MT and MST that relate to heading and describes how feedforward, feedback and lateral brain circuits are used to perform motion estimation calculations. The model retina responds to transients in the input stream, producing moving boundary representations. Model V1 generates a local speed and direction estimate that is noisy due to the neural aperture problem. Model MT+ computes a global motion estimate supporting accurate heading estimation in MSTd. Modulatory attentional feedback from MSTd reduces motion ambiguities in MT+. The model quantitatively simulates properties of human data during heading estimation tasks. Simulated rotations less than 1 degree per second do not affect accuracy, whereas faster simulated rotations do (Royden et al. 1994, Vis Res 34). MT- segments objects using differential motion. Feedback from MSTv to MT- helps resolve the aperture problem and drives global motion capture: When an object moves differently from the background, MT- segments the object and computes accurate estimates of object motion. The resolution of the aperture problem in the model MT- displays the same time-course as primate MT (Pack and Born 2001, Nature 409). Model representations activate processes of visually guided steering and navigation. The model is tested both through simulations of the cellular dynamics of cortical cells and on complex natural image sequences. This distinguishes our model from other models which either include cellular dynamics or focus on real world applications.
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