Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Neural network for optimization of binary computer-generated hologram with printing model

Hopfield neural nets are used to optimize point-oriented binary computer-generated holograms (CGHs). It can be considered as a parallel and iterative \u27halftoning\u27 process in the spatial frequency domain. The results are comparable to other iterative methods but require shorter computation times. In this process, the generation of the CGH by FFT, binarization, and IFFT is viewed as a black box with inputs and outputs consisting of 512 arrays containing an object of size 64 . The neural-network optimization feeds back the Fourier transform of the reconstruction error to update the neuron states, which correspond to the samples of the continuous hologram. To reduce the error of the reconstruction, the input is allowed to deviate from the original array in different specified ways. For example, a previously reported approach using Projection Onto Constraint Sets (POCS) varied only the region of the input array outside the object, while we allow the entire array to be modified, thus providing more freedom in the optimization. The method may be applied either to magnitude-only or phase-only holograms. A modification of the parallel updating function is also reported. Different optimization options are compared. Use of a practical printing model requires optimization under assumed constraints to test the convergence properties of the algorithm

Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyze the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast