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<p>Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence,&#160; vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing. In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks.</p> <p>This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing.</p> <ul> <li>Provides a key knowledge boost to developers of image processing applications</li> <li>Is unique in emphasizing the practical utility of attention mechanisms</li> <li>Includes a number of real-world examples that readers can implement in their own work:</li> <li>robot navigation and object selection</li> <li>image and video quality assessment</li> <li>image and video coding</li> <li>Provides codes for users to apply in practical attentional models and mechanisms</li> </ul>

Elucidating the neural basis of perceptual biases, such as those produced by visual illusions, can provide powerful insights into the neural mechanisms of perceptual inference. However, studying the subjective percepts of animals poses a fundamental challenge: unlike human participants, animals cannot be verbally instructed to report what they see, hear, or feel. Instead, they must be trained to perform a task for reward, and researchers must infer from their responses what the animal perceived. However, animals’ responses are shaped by reward feedback, thus raising the major concern that the reward regimen may alter the animal’s decision strategy or even their intrinsic perceptual biases. Using simulations of a reinforcement learning agent, we demonstrate that conventional reward strategies fail to allow accurate estimation of perceptual biases. We developed a method that estimates perceptual bias during task performance and then computes the reward for each trial based on the evolving estimate of the animal’s perceptual bias. Our approach makes use of multiple stimulus contexts to dissociate perceptual biases from decision-related biases. Starting with an informative prior, our Bayesian method updates a posterior over the perceptual bias after each trial. The prior can be specified based on data from past sessions, thus reducing the variability of the online estimate and allowing it to converge to a stable value over a small number of trials. After validating our method on synthetic data, we apply it to estimate perceptual biases of monkeys in a motion direction discrimination task in which varying background optic flow induces robust perceptual biases. This method overcomes an important challenge to understanding the neural basis of subjective percepts.

The strength of phylogenetic signal in extinction risk can give insight into the mechanisms behind species' declines. Nevertheless, no existing measure of phylogenetic pattern in a binary trait, such as extinction-risk status, measures signal strength in a way that can be compared among data sets. We developed a new measure for phylogenetic signal of binary traits, D, which simulations show gives robust results with data sets of more than 50 species, even when the proportion of threatened species is low. We applied D to the red-list status of British birds and the world's mammals and found that the threat status for both groups exhibited moderately strong phylogenetic clumping. We also tested the hypothesis that the phylogenetic pattern of species threatened by harvesting will be more strongly clumped than for those species threatened by either habitat loss or invasive species because the life-history traits mediating the effects of harvesting show strong evolutionary pattern. For mammals, our results supported our hypothesis; there was significant but weaker phylogenetic signal in the risk caused by the other two drivers (habitat loss and invasive species). We conclude that D is likely to be a useful measure of the strength of phylogenetic pattern in many binary traits.

Many everyday interactions with moving objects benefit from an accurate perception of their movement. Self-motion, however, complicates object motion perception because it generates a global pattern of motion on the observer's retina and radically influences an object's retinal motion. There is strong evidence that the brain compensates by suppressing the retinal motion due to self-motion, however, this requires estimates of depth relative to the object-otherwise the appropriate self-motion component to remove cannot be determined. The underlying neural mechanisms are unknown, but neurons in brain areas MT and MST may contribute given their sensitivity to motion parallax and depth through joint direction, speed, and disparity tuning. We developed a neural model to investigate whether cells in areas MT and MST with well-established neurophysiological properties can account for human object motion judgments during self-motion. We tested the model by comparing simulated object motion signals to human object motion judgments in environments with monocular, binocular, and ambiguous depth. Our simulations show how precise depth information, such as that from binocular disparity, may improve estimates of the retinal motion pattern due the self-motion through increased selectivity among units that respond to the global self-motion pattern. The enhanced self-motion estimates emerged from recurrent feedback connections in MST and allowed the model to better suppress the appropriate direction, speed, and disparity signals from the object's retinal motion, improving the accuracy of the object's movement direction represented by motion signals.

A goal of computational models is not only to explain experimental data but also to make new predictions. A current focus of computational neuroimaging is to predict features of the presented stimulus from measured brain signals. These computational neuroimaging approaches may be agnostic about the underlying neural processes or may be biologically inspired. Here, we use the biologically inspired population receptive field (pRF) approach to identify presented images from fMRI recordings of the visual cortex, using an explicit model of the underlying neural response selectivity. The advantage of the pRF-model is its simplicity: it is defined by a handful of parameters, which can be estimated from fMRI data that was collected within half an hour. Using 7T MRI, we measured responses elicited by different visual stimuli: (i) conventional pRF mapping stimuli, (ii) semi-random synthetic images and (iii) natural images. The pRF mapping stimuli were used to estimate the pRF-properties of each cortical location in early visual cortex. Next, we used these pRFs to identify which synthetic or natural images was presented to the subject from the fMRI responses. We show that image identification using V1 responses is far above chance, both for the synthetic and natural images. Thus, we can identify visual images, including natural images, using the most fundamental low-parameter pRF model estimated from conventional pRF mapping stimuli. This allows broader application of image identification.

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

The flat interface nerve electrode (FINE) has demonstrated significant capability for fascicular and subfascicular stimulation selectivity. However, due to the inherent complexity of the neuromuscular skeletal systems and nerve–electrode interface, a trajectory tracking motion control algorithm of musculoskeletal systems for functional electrical stimulation using a multiple contact nerve cuff electrode such as FINE has not yet been developed. In our previous study, a control system was developed for multiple-input multiple-output (MIMO) musculoskeletal systems with little prior knowledge of the system. In this study, more realistic computational ankle/subtalar joint model including a finite element model of the sciatic nerve was developed. The control system was tested to control the motion of ankle/subtalar joint angles by modulating the pulse amplitude of each contact of a FINE placed on the sciatic nerve. The simulation results showed that the control strategy based on the separation of steady state and dynamic properties of the system resulted in small output tracking errors for different reference trajectories such as sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against external disturbances and system parameter variations such as muscle fatigue. These simulation results under various circumstances indicate that it is possible to take advantage of multiple contact nerve electrodes with spatial selectivity for the control of limb motion by peripheral nerve stimulation even with limited individual muscle selectivity. This technology could be useful to restore neural function in patients with paralysis.

Physical and biological disturbances are ecological processes affecting patterns in biodiversity at a range of scales in a variety of terrestrial and aquatic systems. Theoretical and empirical evidence suggest that effects of disturbance on diversity differ qualitatively and quantitatively, depending on levels of productivity (e.g., the dynamic equilibrium model). In this study we contrasted the interactive effects between physical disturbance and productivity to those between biological disturbance and productivity. Furthermore, to evaluate how these effects varied among different components of marine hard-substratum assemblages, analyses were done separately on algal and invertebrate richness, as well as richness of the whole assemblage. Physical disturbance (wave action) was simulated at five distinct frequencies, while biological disturbance (grazing periwinkles) was manipulated as present or absent, and productivity was manipulated as high or ambient. Uni- and multivariate analyses both showed significant effects of physical disturbance and interactive effects between biological disturbance and productivity on the composition of assemblages and total species richness. Algal richness was significantly affected by productivity and biological disturbance, whereas invertebrate richness was affected by physical disturbance only. Thus, we show, for the first time, that biological disturbance and physical disturbance interact differently with productivity, because these two types of disturbances affect different components of assemblages. These patterns might be explained by differences in the distribution (i.e., press vs. pulse) and degree of selectivity between disturbances. Because different types of disturbance can affect different components of assemblages, general ecological models will benefit from using natural diverse communities, and studies concerned with particular subsets of assemblages may be misleading. In conclusion, this study shows that the outcome of experiments on effects of disturbance and productivity on diversity is greatly influenced by the composition of the assemblage under study, as well as on the type of disturbance that is used as an experimental treatment.

In this paper, we report the analysis of seven benzopyridoindole and benzopyridoquinoxaline drugs binding to different duplex DNA and triple helical DNA, using an approach combining electrospray ionization mass spectrometry (ESI-MS), tandem mass spectrometry (MS/MS), and molecular modeling. The ligands were ranked according to the collision energy (CE 50) necessary to dissociate 50% of the complex with the duplex or the triplex in tandem MS. To determine the probable ligand binding site and binding mode, molecular modeling was used to calculate relative ligand binding energies in different binding sites and binding modes. For duplex DNA binding, the ligand-DNA interaction energies are roughly correlated with the experimental CE 50, with the two benzopyridoindole ligands more tightly bound than the benzopyridoquinoxaline ligands. There is, however, no marked AT versus GC base preference in binding, as supported both by the ESI-MS and the calculated ligand binding energies. Product ion spectra of the complexes with triplex DNA show only loss of neutral ligand for the benzopyridoquinoxalines, and loss of the third strand for the benzopyridoindoles, the ligand remaining on the duplex part. This indicates a higher binding energy of the benzopyridoindoles, and also shows that the ligands interact with the triplex via the duplex. The ranking of the ligand interaction energies compared with the CE 50 values obtained by MS/MS on the complexes with the triplex clearly indicates that the ligands intercalate via the minor groove of the Watson-Crick duplex. Regarding triplex versus duplex selectivity, our experiments have demonstrated that the most selective drugs for triplex share the same heteroaromatic core.