Relational color constancy in achromatic and isoluminant images

Relational color constancy, which refers to the constancy of perceived relations between surface colors under changes in illuminant, may be based on the computation of spatial ratios of cone excitations. As this activity need occur only within rather than between cone pathways, relational color constancy might be assumed to be based on relative luminance processing. This hypothesis was tested in a psychophysical experiment in which observers viewed simulated images of Mondrian patterns undergoing colorimetric changes that could be attributed either to an illuminant change or to a nonilluminant change; the images were isoluminant, achromatic, or unmodified. Observers reliably discriminated the two types of changes in all three conditions, implying that relational color constancy is not based on luminance cues alone. A computer simulation showed that in these isoluminant and achromatic images spatial ratios of cone excitations and of combinations of cone excitations were almost invariant under illuminant changes and that discrimination performance could be predicted from deviations in these ratios.Biotechnology and Biological Sciences Research Council (BBSRC

Detecting changes of spatial cone-excitation ratios in dichoptic viewing

AbstractSpatial ratios of cone excitations produced by light reflected by different surfaces in a scene may provide the cue for discriminating changes in illuminant from changes in surface reflectances. To test whether these ratios can be computed across the two eyes, observers were presented with simulations on a computer-controlled monitor of pairs of juxtaposed or separated Munsell surfaces undergoing an illuminant change with a small change in cone-excitation ratios or a change with constant cone-excitation ratios. Surfaces were viewed either binocularly or dichoptically. Observers reliably discriminated the two changes in both viewing conditions, although less well dichoptically. Cone-excitation ratios, which may in principle be computed retinally, may also be computed cortically

Extensions of independent component analysis for natural image data

An understanding of the statistical properties of natural images is useful for any kind of processing to be performed on them. Natural image statistics are, however, in many ways as complex as the world which they depict. Fortunately, the dominant low-level statistics of images are sufficient for many different image processing goals. A lot of research has been devoted to second order statistics of natural images over the years. Independent component analysis is a statistical tool for analyzing higher than second order statistics of data sets. It attempts to describe the observed data as a linear combination of independent, latent sources. Despite its simplicity, it has provided valuable insights of many types of natural data. With natural image data, it gives a sparse basis useful for efficient description of the data. Connections between this description and early mammalian visual processing have been noticed. The main focus of this work is to extend the known results of applying independent component analysis on natural images. We explore different imaging techniques, develop algorithms for overcomplete cases, and study the dependencies between the components by using a model that finds a topographic ordering for the components as well as by conditioning the statistics of a component on the activity of another. An overview is provided of the associated problem field, and it is discussed how these relatively small results may eventually be a part of a more complete solution to the problem of vision.reviewe

Neural mechanisms of divided feature-selective attention to colour

The work was partly supported by an Experimental Psychology Society summer studentship to Karol Puch under the supervision of JM.Peer reviewedPostprin

Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph

The time-course of colour vision

Four experiments are presented, each investigating temporal properties of colour vision processing in human observers. The first experiment replicates and extends an experiment by Stromeyer et al. (1991). We look for a phase difference between combined temporal modulations in orthogonal directions in colour space, which might null the often-claimed latency of signals originating from the short-wavelength sensitive cones (S-cones). We provide another estimate of the magnitude of this latency, and give evidence to suggest that it originates early in the chromatic pathway, before signals from S-cones are combined with those that receive opposed L- and M-cone input. In the second experiment we adapt observers to two stimuli that are matched in the mean and amplitude of modulation they offer to the cone classes and to the cardinal opponent mechanisms, but that differ in chromatic appearance, and hence their modulation of later colour mechanisms. Chromatic discrimination thresholds after adaptation to these two stimuli differ along intermediate directions in colour space, and we argue that these differences reveal the adaptation response of central colour mechanisms. In the third experiment we demonstrate similar adaptation using the same stimuli, measured with reaction times rather than thresholds. In the final experiment, we measure the degree to which colour constancy is achieved as a function of time in a simulated stimulus environment in which the illuminant changes periodically. We find that perfect constancy is not achieved instantaneously after an illuminant chromaticity shift and that constancy of colour appearance judgements increases over several seconds

Software Defined Radio Solutions for Wireless Communications Systems

Wireless technologies have been advancing rapidly, especially in the recent years. Design, implementation, and manufacturing of devices supporting the continuously evolving technologies require great efforts. Thus, building platforms compatible with different generations of standards and technologies has gained a lot of interest. As a result, software defined radios (SDRs) are investigated to offer more flexibility and scalability, and reduce the design efforts, compared to the conventional fixed-function hardware-based solutions.This thesis mainly addresses the challenges related to SDR-based implementation of today’s wireless devices. One of the main targets of most of the wireless standards has been to improve the achievable data rates, which imposes strict requirements on the processing platforms. Realizing real-time processing of high throughput signal processing algorithms using SDR-based platforms while maintaining energy consumption close to conventional approaches is a challenging topic that is addressed in this thesis.Firstly, this thesis concentrates on the challenges of a real-time software-based implementation for the very high throughput (VHT) Institute of Electrical and Electronics Engineers (IEEE) 802.11ac amendment from the wireless local area networks (WLAN) family, where an SDR-based solution is introduced for the frequency-domain baseband processing of a multiple-input multipleoutput (MIMO) transmitter and receiver. The feasibility of the implementation is evaluated with respect to the number of clock cycles and the consumed power. Furthermore, a digital front-end (DFE) concept is developed for the IEEE 802.11ac receiver, where the 80 MHz waveform is divided to two 40 MHz signals. This is carried out through time-domain digital filtering and decimation, which is challenging due to the latency and cyclic prefix (CP) budget of the receiver. Different multi-rate channelization architectures are developed, and the software implementation is presented and evaluated in terms of execution time, number of clock cycles, power, and energy consumption on different multi-core platforms.Secondly, this thesis addresses selected advanced techniques developed to realize inband fullduplex (IBFD) systems, which aim at improving spectral efficiency in today’s congested radio spectrum. IBFD refers to concurrent transmission and reception on the same frequency band, where the main challenge to combat is the strong self-interference (SI). In this thesis, an SDRbased solution is introduced, which is capable of real-time mitigation of the SI signal. The implementation results show possibility of achieving real-time sufficient SI suppression under time-varying environments using low-power, mobile-scale multi-core processing platforms. To investigate the challenges associated with SDR implementations for mobile-scale devices with limited processing and power resources, processing platforms suitable for hand-held devices are selected in this thesis work. On the baseband processing side, a very long instruction word (VLIW) processor, optimized for wireless communication applications, is utilized. Furthermore, in the solutions presented for the DFE processing and the digital SI canceller, commercial off-the-shelf (COTS) multi-core central processing units (CPUs) and graphics processing units (GPUs) are used with the aim of investigating the performance enhancement achieved by utilizing parallel processing.Overall, this thesis provides solutions to the challenges of low-power, and real-time software-based implementation of computationally intensive signal processing algorithms for the current and future communications systems

Go and No-go Learning in Reward and Punishment: Interactions between Affect and Effect

Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively