Random on-board pixel sampling (ROPS) X-ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.Comment: 9 pages, 6 figures, Presented in 19th iWoRI

Neural mechanisms of reactivation-induced updating that enhance and distort memory

We remember a considerable number of personal experiences because we are frequently reminded of them, a process known as memory reactivation. Although memory reactivation helps to stabilize and update memories, reactivation may also introduce distortions if novel information becomes incorporated with memory. Here we used functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms mediating reactivation-induced updating in memory for events experienced during a museum tour. During scanning, participants were shown target photographs to reactivate memories from the museum tour followed by a novel lure photograph from an alternate tour. Later, participants were presented with target and lure photographs and asked to determine whether the photographs showed a stop they visited during the tour. We used a subsequent memory analysis to examine neural recruitment during reactivation that was associated with later true and false memories. We predicted that the quality of reactivation, as determined by online ratings of subjective recollection, would increase subsequent true memories but also facilitate incorporation of the lure photograph, thereby increasing subsequent false memories. The fMRI results revealed that the quality of reactivation modulated subsequent true and false memories via recruitment of left posterior parahippocampal, bilateral retrosplenial, and bilateral posterior inferior parietal cortices. However, the timing of neural recruitment and the way in which memories were reactivated contributed to differences in whether memory reactivation led to distortions or not. These data reveal the neural mechanisms recruited during memory reactivation that modify how memories will be subsequently retrieved, supporting the flexible and dynamic aspects of memory

Low-frequency oscillatory correlates of auditory predictive processing in cortical-subcortical networks: a MEG-study

Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4–8/8–13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

Alpha Rhythms Reveal When and Where Item and Associative Memories Are Retrieved

Memories for past experiences can range from vague recognition to full-blown recall of associated details. Electroencephalography has shown that recall signals unfold a few hundred milliseconds after simple recognition, but has only provided limited insights into the underlying brain networks. Functional magnetic resonance imaging (fMRI) has revealed a “core recollection network” (CRN) centered on posterior parietal and medial temporal lobe regions, but the temporal dynamics of these regions during retrieval remain largely unknown. Here we used Magnetoencephalography in a memory paradigm assessing correct rejection (CR) of lures, item recognition (IR) and associative recall (AR) in human participants of both sexes. We found that power decreases in the alpha frequency band (10–12 Hz) systematically track different mnemonic outcomes in both time and space: Over left posterior sensors, alpha power decreased in a stepwise fashion from 500 ms onward, first from CR to IR and then from IR to AR. When projecting alpha power into source space, the CRN known from fMRI studies emerged, including posterior parietal cortex (PPC) and hippocampus. While PPC showed a monotonic change across conditions, hippocampal effects were specific to recall. These region-specific effects were corroborated by a separate fMRI dataset. Importantly, alpha power time courses revealed a temporal dissociation between item and associative memory in hippocampus and PPC, with earlier AR effects in hippocampus. Our data thus link engagement of the CRN to the temporal dynamics of episodic memory and highlight the role of alpha rhythms in revealing when and where different types of memories are retrieved

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data

Cortical region interactions and the functional role of apical dendrites

The basal and distal apical dendrites of pyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation of input sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets of inputs can be integrated independently has the potential to operate in a variety of ways which are not possible for the conventional model of a neuron in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate affects on neural activity and learning

Involvement of hippocampal subfields and anterior-posterior subregions in encoding and retrieval of item, spatial, and associative memories: Longitudinal versus transverse axis

The functional role of the hippocampal formation in episodic memory has been studied using functional magnetic resonance imaging (fMRI) for many years. The hippocampus can be segmented into three major anteroposterior sections, called head, body and tail, and into the Cornu Ammonis (CA), dentate gyrus (DG), and subiculum (Sub) subfields based on its transverse axis. However, the exact role of these subregions and subfields in memory processes is less understood. In the present study we combined ultra-high resolution structural Magnetic Resonance Imaging (MRI) at 4.7 T with an event-related high-resolution fMRI paradigm based on the ‘Designs’ subtest of the Wechsler Memory Scale to investigate how the hippocampal subfields and longitudinal subregions are involved in encoding and retrieval of item, spatial, and associative memories. Our results showed that during memory encoding, regardless of the type of memory being learned, all subregions and all subfields were active. During the retrieval phase, on the other hand, we observed an anterior to posterior gradient in hippocampal activity for all subfields and all types of memory. Our findings also confirmed presence of an anterior to posterior gradient in hippocampal activity during spatial learning. Comparing subfield activities to each other revealed that the DG was more active than the CA1-3 and Sub during both encoding and retrieval. Finally, our results showed that for every subfield, encoding vs. retrieval activity differences were larger in the hippocampal head than in the hippocampal body and tail. Furthermore, these encoding vs. retrieval activity differences were similar in all subfields, highlighting the importance of studying both the longitudinal and transverse axis specialization simultaneously. Current findings further elucidate the structure–function relationship between the human hippocampus and episodic memory

Naïve to expert: Considering the role of previous knowledge in memory.:Considering the role of previous knowledge in memory

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