Cellular Neural Networks with Switching Connections

Artificial neural networks are widely used for parallel processing of data analysis and visual information. The most prominent example of artificial neural networks is a cellular neural network (CNN), composed from two-dimensional arrays of simple first-order dynamical systems (“cells”) that are interconnected by wires. The information, to be processed by a CNN, represents the initial state of the network, and the parallel information processing is performed by converging to one of the stable spatial equilibrium states of the multi-stable CNN. This thesis studies a specific type of CNNs designed to perform the winner-take-all function of finding the largest among the n numbers, using the network dynamics. In a wider context, this amounts to automatically detecting a target spot in the given visual picture. The research, reported in this thesis, demonstrates that the addition of fast on-off switching (blinking) connections significantly improves the functionality of winner-take-all CNNs. Numerical calculations are performed to reveal the dependence of the probability, that the CNN correctly classifies the largest number, on the switching frequency

A Data-Driven Approach to Morphogenesis under Structural Instability

Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design

Voluntary control of long-range motion integration via selective attention to context

Ambiguous stimuli can look different in different contexts. Here we demonstrate that subjective appearance of motion depends not only on current visual input but critically on which aspects of the context are attended. Observers fixated a central oblique test grating flanked by two pairs of orthogonally oriented context gratings arranged in a cross (+) configuration. Each context pair could induce the test stimulus to appear to switch from diagonal motion to either horizontal motion (due to one context pair) or vertical motion (due to the other). Spontaneous switching between these motion states was observed under free viewing. We demonstrate that observers can voluntarily select between specific states when cued to attend selectively to one or other context pair in an alternating manner. Concurrent reports of perceived test stimulus motion depended specifically on which context was currently attended, indicating a high degree of “cued-control” over subjective state via attended context. Further experiments established that the perception was nevertheless still constrained by physical stimulus context as well as by attentional selection among that context. Moreover, the attentional control evident here did not seem reducible solely to local contrast gain modulation of the attended vs. ignored context elements. Selective attention to different parts of the context can evidently resolve the ambiguity of the test grating, with integration arising selectively for those components that are jointly attended. Such selective integration can result in substantial voluntarily controlled changes in phenomenal perception

Right Parietal Brain Activity Precedes Perceptual Alternation of Bistable Stimuli

Momentary fluctuations of baseline activity have been shown to influence responses to sensory stimulation both behaviorally and neurophysiologically. This suggests that perceptual awareness does not solely arise from physical stimulus properties. Here we studied whether the momentary state of the brain immediately before stimulus presentation indicates how a physically unique but perceptually ambiguous stimulus will be perceived. A complex Necker cube was intermittently presented and subjects indicated whether their perception changed with respect to the preceding presentation. EEG was recorded from 256 channels. The prestimulus brain-state was defined as the spatial configuration of the scalp potential map within the 50 ms before stimulus arrival, representing the sum of all momentary ongoing brain processes. Two maps were found that doubly dissociated perceptual reversals from perceptual stability. For EEG sweeps classified as either map, distributed inverse solutions were computed and statistically compared. This yielded activity confined to a region in right inferior parietal cortex that was significantly more active before a perceptual reversal. In contrast, no significant topographic differences of the evoked potentials elicited by stable vs. reversed Necker cubes were found. This indicates that prestimulus activity in right inferior parietal cortex is associated with the perceptual chang

Dynamics analysis and applications of neural networks

Ph.DDOCTOR OF PHILOSOPH

Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning

The role of previous experience in conscious perception

Which factors determine whether a stimulus is consciously perceived or unconsciously processed? Here, I investigate how previous experience on two different time scales – long term experience over the course of several days, and short term experience based on the previous trial – impact conscious perception. Regarding long term experience, I investigate how perceptual learning does not only change the capacity to process stimuli, but also the capacity to consciously perceive them. To this end, subjects are trained extensively to discriminate between masked stimuli, and concurrently rate their subjective experience. Both the ability to discriminate the stimuli as well as subjective awareness of the stimuli increase as a function of training. However, these two effects are not simple byproducts of each other. On the contrary, they display different time courses, with above chance discrimination performance emerging before subjective experience; importantly, the two learning effects also rely on different circuits in the brain: Moving the stimuli outside the trained receptive field size abolishes the learning effects on discrimination ability, but preserves the learning effects on subjective awareness. This indicates that the receptive fields serving subjective experience are larger than the ones serving objective performance, and that the channels through which they receive their information are arranged in parallel. Regarding short term experience, I investigate how memory based predictions arising from information acquired on the trial before affect visibility and the neural correlates of consciousness. To this end, I vary stimulus evidence as well as predictability and acquire electroencephalographic data. A comparison of the neural processes distinguishing consciously perceived from unperceived trials with and without predictions reveals that predictions speed up processing, thus shifting the neural correlates forward in time. Thus, the neural correlates of consciousness display a previously unappreciated flexibility in time and do not arise invariably late as had been predicted by some theorists. Admittedly, however, previous experience does not always stabilize perception. Instead, previous experience can have the reverse effect: Seeing the opposite of what was there, as in so-called repulsive aftereffects. Here, I investigate what determines the direction of previous experience using multistable stimuli. In a functional magnetic resonance imaging experiment, I find that a widespread network of frontal, parietal, and ventral occipital brain areas is involved in perceptual stabilization, whereas the reverse effect is only evident in extrastriate cortex. This areal separation possibly endows the brain with the flexibility to switch between exploiting already available information and emphasizing the new. Taken together, my data show that conscious perception and its neuronal correlates display a remarkable degree of flexibility and plasticity, which should be taken into account in future theories of consciousness

Visual Cortex

The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences

Bridging structure and function with brain network modeling

High-throughput neuroimaging technology enables rapid acquisition of vast amounts of structural and functional data on multiple spatial and temporal scales. While novel methods to extract information from these data are continuously developed, there is no principled approach for the systematic integration of distinct experimental results into a common theoretical framework, yet. The central result of this dissertation is a biophysically-based framework for brain network modeling that links structural and functional data across scales and modalities and integrates them with dynamical systems theory. Specifically, the publications in this thesis i. introduce an automated pipeline that extracts structural and functional information from multimodal imaging data to construct and constrain brain models, ii. link whole-brain models with empirical EEG-fMRI (simultaneous electroencephalography and functional magnetic resonance imaging) data to integrate neural signals with simulated activity, iii. propose a framework for reverse-engineering neurophysiological dynamics and mechanisms underlying commonly observed features of neural activity, iv. document a software module that makes users acquainted with theory and practice of brain modeling, v. associate aging with structural and functional connectivity and vi. examine how parcellation size and short-range connectivity affect model dynamics. Taken together, these results form a novel approach that enables reverse-engineering of neurophysiological processes and mechanisms on the basis of biophysically-based brain models.Zusammenfassung Hochdurchsatzverfahren zur neuronalen Bildgebung ermöglichen die schnelle Erfassung großer Mengen an strukturellen und funktionellen Daten über verschiedenen räumlichen und zeitlichen Skalen. Obwohl ständig neue Methoden zur Verarbeitung der in diesen Daten enthaltenen Informationen entwickelt werden gibt es bisher kein systematisches Verfahren um experimentelle Ergebnisse in einem gemeinsamen theoretischen Rahmenwerk zu integrieren und zu verknüpfen. Das Hauptergebnis dieser Dissertation ist ein biophysikalisch basiertes Gehirn- Netzwerkmodell das strukturelle und funktionelle Daten über verschiedene Skalen und Modalitäten hinweg verknüpft und mit dynamischer Systemtheorie vereint. Die hier zusammengefassten Publikationen i. stellen eine automatische Software-Pipeline vor die strukturelle und funktionelle Informationen aus multimodalen Bilddaten extrahiert um Gehirnmodelle zu konstruieren und zu parametrisieren, ii. verknüpfen Ganzhi rnmodel le mi t empi r i schen EEG- fMRT ( s imul tane Elektroenzephalographie und funktionelle Magnetresonanztomographie) Daten um neuronale Signale mit simulierter Aktivität zu integrieren, iii. schlagen ein Rahmenwerk vor um neurophysiologische Dynamiken und Mechanismen die häufig beobachteten Eigenschaften neuronaler Aktivität zu Grunde liegen zu rekonstruieren, iv. dokumentieren ein Software-Modul das Benutzer mit Theorie und Praxis der Gehirnmodellierung vertraut macht, v. assoziieren Alterungsprozesse mit struktureller und funktioneller Konnektivität und vi. untersuchen wie Gehirn-Parzellierung und lokale Konnektivität die Modelldynamik beeinflussen. Zusammengenommen ergibt sich ein neuartiges Verfahren das die Rekonstruktion neurophysiologischer Prozesse und Mechanismen ermöglicht und mit dessen Hilfe neuronale Aktivität auf verschiedenen räumlichen und zeitlichen Skalen anhand biophysikalisch basierter Modelle vorhersagt werden kann

Perceptual grouping by proximity and orientation bias: experimental and modelling investigations

Grouping by proximity is the principle of perceptual organization by which the elements of a visual scene which are closer in space tend to be perceived as a coherent ensemble. Research into this topic makes substantial use of the class of stimuli known as dot lattices. The Pure Distance Law (Kubovy et al., 1998) predicts that the probability of grouping by proximity in these stimuli only depends on the relative inter-dot distance between competing organizations. Despite much effort to explain how grouping by proximity is shaped by the basic organization of visual stimuli, its neural mechanisms are still under debate. Moreover, previous studies reported that grouping in dot lattices also occurs according to an orientation bias, by which these stimuli are perceived along a preferred orientation (vertical), regardless of what predicted by the Pure Distance Law. The aim of this thesis is to shed light on the functional and neural mechanisms characterizing grouping by proximity in dot lattices, as well as the trade-off between proximity- and orientation-based grouping. Study 1 investigates the role of high-level visual working memory (VWM) in promoting for the shift between grouping by proximity and orientation bias. Both the quantity (load) and the quality (content) of the information stored in VWM shape online grouping for dot lattices. Study 2 presents a neural network model simulating the dynamics occurring between low- and high-level processing stages during dot lattices perception. The degree of synchrony between the units at low-level module has a key role in accounting for grouping by proximity. Overall, our results show that high-level (Study 1) and low-level (Study 2) operations contribute in parallel to the emergence of grouping by proximity, as well as to its reciprocity with orientation-based grouping