Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective. We introduce an integrated deep neural network architecture for modeling ESM. It learns to estimate the occupancy state of the world and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment. During the exploration, our proposed ESM model updates belief of the global map based on local observations using a recurrent neural network. It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places over longterm exploration in large environments which enables agents to perform place recognition and hence, loop closure. Our proposed ESM network contributes in the following aspects: (1) without feature engineering, our model predicts free space based on egocentric views efficiently in an end-to-end manner; (2) different from other deep learning-based mapping system, ESMN deals with continuous actions and states which is vitally important for robotic control in real applications. In the experiments, we demonstrate its accurate and robust global mapping capacities in 3D virtual mazes and realistic indoor environments by comparing with several competitive baselines.

This paper presents a current-mode neural/muscular stimulator with an exponentially decaying stimulation current. The use of exponentially decaying current makes the voltage on the stimulating electrode constant during the stimulation, which eliminates the headroom and increases the power efficiency. A simple exponentially decaying current generator is proposed based on Taylor series approximation and implemented in a 16-channel prototype stimulator IC. The prototype IC is fabricated in a 0.18-μm CMOS process with high-voltage LDMOS option, occupying a core area of 1.65 mm × 1.65 mm. The stimulator is tested with different loads, which mimics the electrode impedances, and the measured results show that maximum stimulation power efficiency of 95.9% can be achieved at the output stage of the stimulator. Depending on the electrode impedance and stimulation current, the power efficiency can be improved by nearly 10% at the output stage, compared to traditional constant-current stimulator.

The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.

Image appeal is determined by factors such as exposure, white balance, motion blur, scene perspective, and semantics. All these factors influence the selection of the best image(s) in a typical photo triaging task. This paper presents the results of an exploratory study on how image appeal affected selection behavior and visual attention patterns of 11 users, who were assigned the task of selecting the best photo from each of 40 groups. Images with low appeal were rejected, while highly appealing images were selected by a majority. Images with higher appeal attracted more visual attention, and users spent more time exploring them. A comparison of user eye fixation maps with three state-of-the-art saliency models revealed that these differences are not captured by the models.

Recently, we proposed a cortical-based model that accounts for a range of psychophysical results on contour salience [S.C. Yen, L.H. Finkel, Vision Res. 38 (1998) 719–741]. In this model, cortical units are interconnected by long-range horizontal connections where the connection strength between units is defined by an inter-element spatial connectivity function. In the present work, we determine the spatial connectivity function psychophysically and compare the result to the function implemented in the original computational model. The results suggest the original product of gaussian spatial weighting function does not completely describe the psychophysical data. Rather, we find that the connectivity function is “tapered” suggesting a lack of separability between position and orientation in cortical connectivity.

One advantage of cortical synchronization as a binding mechanism is its ability to account for phenomena such as perceptual grouping. Such a mechanism requires the ability to synchronize groups of cells and to desynchronize these groups from each other. We present a striate cortical model of perceptual grouping in which synchronization and desynchronization is carried out by a single, common mechanism. Cortical pyramidal cells and interneurons are simulated using multi-compartmental models. Cells in different orientation columns are inter-connected via two sets of long-distance connections that differ in axonal delays and spatial projections. The relative influence of these two connections determines whether synchronization or desynchronization occurs. Once one group of cells synchronizes, inputs from these cells facilitate synchronization in other orientation columns. We address the role of these synchronizing and desynchronizing connections in mediating perceptual grouping and metastable percepts.

We present a model of contour extraction in which the perceptual salience of contours arises from long-range interactions between orientation-selective filters. It has been previously shown that salient contours may be extracted from noisy images by using a number heuristic features. Our algorithm is based on cortical mechanisms, and simulations show close agreement with results from recent anatomical, physiological and psychophysical studies including recent results of D.J. Field et al. (1993), I. Kovacs et al., and M.K. Kapadia et al. (1995). The performance of the algorithm is demonstrated on a range of psychophysical stimuli and real images.