A Neural Model of Surface Perception: Lightness, Anchoring, and Filling-in

This article develops a neural model of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models have clarified how the brain can compute the relative contrast of images from variably illuminate scenes. How the brain determines an absolute lightness scale that "anchors" percepts of surface lightness to us the full dynamic range of neurons remains an unsolved problem. Lightness anchoring properties include articulation, insulation, configuration, and are effects. The model quantatively simulates these and other lightness data such as discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, and the Craik-O'Brien-Cornsweet illusion. The model also clarifies the functional significance for lightness perception of anatomical and neurophysiological data, including gain control at retinal photoreceptors, and spatioal contrast adaptation at the negative feedback circuit between the inner segment of photoreceptors and interacting horizontal cells. The model retina can hereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A later model cortical processing stages, boundary representations gate the filling-in of surface lightness via long-range horizontal connections. Variants of this filling-in mechanism run 100-1000 times faster than diffusion mechanisms of previous biological filling-in models, and shows how filling-in can occur at realistic speeds. A new anchoring mechanism called the Blurred-Highest-Luminance-As-White (BHLAW) rule helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural images under variable lighting conditions.Air Force Office of Scientific Research (F49620-01-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-01-1-0624

A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images

This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.Air Force Office of Scientific Research (F496201-01-1-0397); Defense Advanced Research Project and the Office of Naval Research (N00014-95-0409, N00014-01-1-0624

The Globus Pallidus Sends Reward-Related Signals to the Lateral Habenula

SummaryAs a major output station of the basal ganglia, the globus pallidus internal segment (GPi) projects to the thalamus and brainstem nuclei thereby controlling motor behavior. A less well known fact is that the GPi also projects to the lateral habenula (LHb) which is often associated with the limbic system. Using the monkey performing a saccade task with positionally biased reward outcomes, we found that antidromically identified LHb-projecting neurons were distributed mainly in the dorsal and ventral borders of the GPi and that their activity was strongly modulated by expected reward outcomes. A majority of them were excited by the no-reward-predicting target and inhibited by the reward-predicting target. These reward-dependent modulations were similar to those in LHb neurons but started earlier than those in LHb neurons. These results suggest that GPi may initiate reward-related signals through its effects on the LHb, which then influences the dopaminergic and serotonergic systems

Dopamine-Mediated Learning and Switching in Cortico-Striatal Circuit Explain Behavioral Changes in Reinforcement Learning

The basal ganglia are thought to play a crucial role in reinforcement learning. Central to the learning mechanism are dopamine (DA) D1 and D2 receptors located in the cortico-striatal synapses. However, it is still unclear how this DA-mediated synaptic plasticity is deployed and coordinated during reward-contingent behavioral changes. Here we propose a computational model of reinforcement learning that uses different thresholds of D1- and D2-mediated synaptic plasticity which are antagonized by DA-independent synaptic plasticity. A phasic increase in DA release caused by a larger-than-expected reward induces long-term potentiation (LTP) in the direct pathway, whereas a phasic decrease in DA release caused by a smaller-than-expected reward induces a cessation of long-term depression, leading to LTP in the indirect pathway. This learning mechanism can explain the robust behavioral adaptation observed in a location-reward-value-association task where the animal makes shorter latency saccades to reward locations. The changes in saccade latency become quicker as the monkey becomes more experienced. This behavior can be explained by a switching mechanism which activates the cortico-striatal circuit selectively. Our model also shows how D1- or D2-receptor blocking experiments affect selectively either reward or no-reward trials. The proposed mechanisms also explain the behavioral changes in Parkinson's disease

Implications of soil water repellence for crop growth and nutrition

In water-limited environments, dryland crop and pasture production on water-repellent sandy soils is often constrained by reduced water infiltration, accentuated overland flow and soil erosion, unstable wetting patterns, and the development of preferential flow paths in the soil profile, which consequently cause considerable spatial heterogeneity in soil water content, increased prevalence of isolated dry zones, and decreased overall soil water retention. The same processes are also likely to affect soil nutrient bioavailability and plant nutrient uptake. Indeed, while problems with crop nutrition on water-repellent sandy soils have been reported by many Australian growers, the role of soil water repellence in crop nutrition has not been studied to date and the mechanisms remain unclear. While various methods exist to manage soil water repellence for improving crop and pasture production (e.g., deep soil cultivation, clay spreading, wetting agent application, stimulation of wax-degrading microorganisms, furrow/on-row sowing and water harvesting, and no-tillage and stubble retention), the outcomes for crop nutrition post-amelioration are not well understood. Several field and glasshouse experiments were, therefore, conducted to assess the implications of soil water repellence and its management on crop growth and nutrition on several sandy soil types from the southwest region of Western Australia. Preliminary field results showed that soil water repellence, if left unmanaged, could adversely affect wheat plant density, shoot dry matter production, K nutrition, and grain yield on a Grey Bleached-Ferric Kandosol (deep grey sandy duplex soil) at Meckering with a moderate water repellence value of up to 1.6 M using the molarity of ethanol droplet (MED) test, supporting the hypothesis that soil water repellence can adversely affect crop growth, nutrition, and grain production. However, it was also revealed at another site, with a Ferric Chromosol (sandy loam yellow duplex soil) at Kojonup, that increased soil water repellence could also increase canola plant density, shoot dry matter production, Cu nutrition, and seed yield when sown with 1 L/ha of banded wetting agent, despite prolonged severe water repellence (MED of 3.4 M) throughout the growing season. Although the underlying mechanisms could not be established from this preliminary study, it was concluded that soil water repellence may have both adverse and beneficial implications, but specific effects on nutrient availability in the root zone and crop nutrition were not defined. Additional field studies were conducted to assess the effect of soil management practices (spading, one-way plough, subsoil clay spreading, and blanket applications of wetting agent) to alleviate soil water repellence on crop growth and nutrition. While all treatments except for one-way ploughing alleviated soil water repellence, only spading significantly improved wheat emergence, shoot dry matter, K nutrition, and grain yield on a Grey Tenosol (pale deep sandy soil) at Badgingarra. By contrast, at Moora, one-way plough treatments improved canola shoot dry matter and nutrition (Ca, S, B, Cu, and Zn contents) but did not mitigate severe water-repellence on a Ferric Chromosol (sandy ironstone gravel duplex soil), and had no effect on plant density or seed yield. However, the improvements due to soil cultivation can be attributed to the alleviation of soil compaction, given that the alleviation of soil water repellence by blanket-applied wetting agent (50 L/ha) and subsoil clay spreading treatments (250 t/ha; 50 % clay; 159 mg K/kg) had negligible effect on crop growth, nutrition, and grain production. Alleviation of soil water repellence was, therefore, not important for crop production at the Badgingarra and Moora study sites, presumably due to the presence of other soil constraints. To avoid the confounding effects from multiple limiting factors evident in the field studies, a series of controlled glasshouse experiments were conducted to examine the effects of topsoil water repellence, topsoil thickness, fertiliser placement, variable low water supply, plant density, and/or surface topography on soil water content, soil nutrient availability, and early wheat growth and nutrition in 27 L containers. All glasshouse experiments demonstrated that severely water-repellent topsoil with a wettable furrow, which ensured uniform seedling emergence, significantly increased wheat seedling development, tiller number, shoot dry matter production, and nutrition (especially N, P, and K) during the early vegetative stage in wheat (40-51 DAS), under low but regular water supply (3.4-5.4 mm every two days). The growth stimulation was attributed to in situ water harvesting caused by preferential flow in the wettable furrow which increased the soil wetting and root depth relative to the completely wettable topsoil treatments that exhibited an even but shallow wetting depth. The even but shallow wetting patterns in completely wettable treatments consequently led to an overall decrease in plant-available water and plant water use efficiency, resulting in poor wheat growth and nutrition, especially under a limited water supply. These findings underscore the high efficacy of in situ water harvesting for improving early wheat growth and nutrition on water-repellent soils relative to completely wettable soils, thus demonstrating a beneficial role of soil water repellence in crop growth and nutrition. Adopting in situ water harvesting principles (i.e., furrow sowing, banding wetting agent in the furrow, and using winged knife-points and/or press-wheels) can, therefore, be an effective strategy for managing crop growth and nutrition on water-repellent sandy soils by maximising the use efficiency of limited soil water supply during the crop establishment period

Variable Input Space Observer for Structural Health Monitoring of High-Rate Systems

Some engineering systems experiencing high-rate dynamic events, including air bags, debris detection, and active blast protection systems, could benefit from high-rate real-time observers for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper discusses critical challenges in designing highrate observers, and argues that adaptive observers are promising at the task of state estimation, and that they could be applied at high-rates provided the development of strategies to substantially increase their convergence. A novel adaptive observer termed a variable input observer (VIO) is studied for its performance and its application for fault detection. The VIO is designed with an adaptive input space, where the hyperspace and coefficients of the state estimation function can change based on the complexity of the dynamic input. Results show good convergence of the VIO versus fixed input strategies

Study of Input Space for State Estimation of High-Rate Dynamics

High‐rate dynamic systems are defined as systems being exposed to highly dynamic environments that comprise high‐rate and high‐amplitude events. Examples of such systems include civil structures exposed to blast, space shuttles prone to debris strikes, and aerial vehicles experiencing in‐flight changes. The high‐rate dynamic characteristics of these systems provides several possibilities for state estimators to improve performance, including a high potential to reduce injuries and save lives. In this paper, opportunities and challenges that are specific to state estimation of high‐rate dynamic systems are presented and discussed. It is argued that a possible path to design of state estimators for high‐rate dynamics is the utilization of adaptive data‐based observers but that further research needs to be conducted to increase their convergence rate. An adaptive neuro‐observer is designed to examine the particular challenges in selecting an appropriate input space in high‐rate state estimation. It is found that the choice of inputs has a significant influence on the observer performance for high‐rate dynamics when compared against a low‐rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision‐making process in a closed‐loop system in making bad judgments

A Processor Core Model for Quantum Computing

We describe an architecture based on a processing 'core' where multiple qubits interact perpetually, and a separate 'store' where qubits exist in isolation. Computation consists of single qubit operations, swaps between the store and the core, and free evolution of the core. This enables computation using physical systems where the entangling interactions are 'always on'. Alternatively, for switchable systems our model constitutes a prescription for optimizing many-qubit gates. We discuss implementations of the quantum Fourier transform, Hamiltonian simulation, and quantum error correction.Comment: 5 pages, 2 figures; improved some arguments as suggested by a refere