Brain Learning, Attention, and Consciousness

The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what is there in the outside world. It is suggested that all conscious states in the brain are resonant states, and that these resonant states trigger learning of sensory and cognitive representations. The model which summarize these concepts are therefore called Adaptive Resonance Theory, or ART, models. Psychophysical and neurobiological data in support of ART are presented from early vision, visual object recognition, auditory streaming, variable-rate speech perception, somatosensory perception, and cognitive-emotional interactions, among others. It is noted that ART mechanisms seem to be operative at all levels of the visual system, and it is proposed how these mechanisms are realized by known laminar circuits of visual cortex. It is predicted that the same circuit realization of ART mechanisms will be found in the laminar circuits of all sensory and cognitive neocortex. Concepts and data are summarized concerning how some visual percepts may be visibly, or modally, perceived, whereas amoral percepts may be consciously recognized even though they are perceptually invisible. It is also suggested that sensory and cognitive processing in the What processing stream of the brain obey top-down matching and learning laws that arc often complementary to those used for spatial and motor processing in the brain's Where processing stream. This enables our sensory and cognitive representations to maintain their stability a.s we learn more about the world, while allowing spatial and motor representations to forget learned maps and gains that are no longer appropriate as our bodies develop and grow from infanthood to adulthood. Procedural memories are proposed to be unconscious because the inhibitory matching process that supports these spatial and motor processes cannot lead to resonance.Defense Advance Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657); National Science Foundation (IRI-97-20333

Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks

The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image \u27signature\u27 based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard\u27s Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye\u27s intensity delineation capability in grayscale image segmentation tasks

Neural models of learning and visual grouping in the presence of finite conduction velocities

The hypothesis of object binding-by-synchronization in the visual cortex has been supported by recent experiments in awake monkeys. They demonstrated coherence among gamma-activities (30–90 Hz) of local neural groups and its perceptual modulation according to the rules of figure-ground segregation. Interactions within and between these neural groups are based on axonal spike conduction with finite velocities. Physiological studies confirmed that the majority of transmission delays is comparable to the temporal scale defined by gamma-activity (11–33 ms). How do these finite velocities influence the development of synaptic connections within and between visual areas? What is the relationship between the range of gamma-coherence and the velocity of signal transmission? Are these large temporal delays compatible with recently discovered phenomenon of gamma-waves traveling across larger parts of the primary visual cortex? The refinement of connections in the immature visual cortex depends on temporal Hebbian learning to adjust synaptic efficacies between spiking neurons. The impact of constant, finite, axonal spike conduction velocities on this process was investigated using a set of topographic network models. Random spike trains with a confined temporal correlation width mimicked cortical activity before visual experience. After learning, the lateral connectivity within one network layer became spatially restricted, the width of the connection profile being directly proportional to the lateral conduction velocity. Furthermore, restricted feedforward divergence developed between neurons of two successive layers. The size of this connection profile matched the lateral connection profile of the lower layer neuron. The mechanism in this network model is suitable to explain the emergence of larger receptive fields at higher visual areas while preserving a retinotopic mapping. The influence of finite conduction velocities on the local generation of gamma-activities and their spatial synchronization was investigated in a model of a mature visual area. Sustained input and local inhibitory feedback was sufficient for the emergence of coherent gamma-activity that extended across few millimeters. Conduction velocities had a direct impact on the frequency of gamma-oscillations, but did neither affect gamma-power nor the spatial extent of gamma-coherence. Adding long-range horizontal connections between excitatory neurons, as found in layer 2/3 of the primary visual cortex, increased the spatial range of gamma-coherence. The range was maximal for zero transmission delays, and for all distances attenuated with finite, decreasing lateral conduction velocities. Below a velocity of 0.5 m/s, gamma-power and gamma-coherence were even smaller than without these connections at all, i.e., slow horizontal connections actively desynchronized neural populations. In conclusion, the enhancement of gamma-coherence by horizontal excitatory connections critically depends on fast conduction velocities. Coherent gamma-activity in the primary visual cortex and the accompanying models was found to only cover small regions of the visual field. This challenges the role of gamma-synchronization to solve the binding problem for larger object representations. Further analysis of the previous model revealed that the patches of coherent gamma-activity (1.8 mm half-height decline) were part of more globally occurring gamma-waves, which coupled over much larger distances (6.3 mm half-height decline). The model gamma-waves observed here are very similar to those found in the primary visual cortex of awake monkeys, indicating that local recurrent inhibition and restricted horizontal connections with finite axonal velocities are sufficient requirements for their emergence. In conclusion, since the model is in accordance with the connectivity and gamma-processes in the primary visual cortex, the results support the hypothesis that gamma-waves provide a generalized concept for object binding in the visual cortex

A robust FLIR target detection employing an auto-convergent pulse coupled neural network

© 2019 Informa UK Limited, trading as Taylor & Francis Group. Automatic target detection (ATD) of a small target along with its true shape from highly cluttered forward-looking infrared (FLIR) imagery is crucial. FLIR imagery is low contrast in nature, which makes it difficult to discriminate the target from its immediate background. Here, pulse-coupled neural network (PCNN) is extended with auto-convergent criteria to provide an efficient ATD tool. The proposed auto-convergent PCNN (AC-PCNN) segments the target from its background in an adaptive manner to identify the target region when the target is camouflaged or contains higher visual clutter. Then, selection of region of interest followed by template matching is augmented to capture the accurate shape of a target in a real scenario. The outcomes of the proposed method are validated through well-known statistical methods and found superior performance over other conventional methods

Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded to extract texture, acceleration and other higher order visual features. Theorized and experimentally confirmed cortical information linking schemes, such as state dependent modulation and temporal synchronization are used to develop a PCNN-based visual information fusion network. The network is used to fuse the results of several object detection systems for the purpose of enhanced object detection accuracy. On actual mammograms and FLIR images, the network achieves an accuracy superior to any of the individual object detection systems it fused. Last, this research develops the first fully adaptive PCNN. Given only an input and a desired output, the adaptive PCNN will find all parameter values necessary to approximate that desired output

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Oszillatorische Gamma-Band-Aktivität bei der Verarbeitung auditorischer Reize im Kurzzeitgedächtnis im MEG

Recent studies have suggested an important role of cortical gamma oscillatory activity (30-100 Hz) as a correlate of encoding, maintaining and retrieving auditory, visual or tactile information in and from memory. It was shown that these cortical stimulus representations were modulated by attention processes. Gamma-band activity (GBA) occurred as an induced response peaking at approximately 200-300 ms after stimulus presentation. Induced cortical responses appear as non-phase-locked activity and are assumed to reflect active cortical processing rather than passive perception. Induced GBA peaking 200-300 ms after stimulus presentation has been assumed to reflect differences between experimental conditions containing various stimuli. By contrast, the relationship between specific oscillatory signals and the representation of individual stimuli has remained unclear. The present study aimed at the identification of such stimulus-specific gamma-band components. We used magnetoencephalography (MEG) to assess gamma activity during an auditory spatial delayed matching-to-sample task. 28 healthy adults were assigned to one of two groups R and L who were presented with only right- or left-lateralized sounds, respectively. Two sample stimuli S1 with lateralization angles of either 15° or 45° deviation from the midsagittal plane were used in each group. Participants had to memorize the lateralization angle of S1 and compare it to a second lateralized sound S2 presented after an 800-ms delay phase. S2 either had the same or a different lateralization angle as S1. After the presentation of S2, subjects had to indicate whether S1 and S2 matched or not. Statistical probability mapping was applied to the signals at sensor level to identify spectral amplitude differences between 15° and 45° stimuli. We found distinct gamma-band components reflecting each sample stimulus with center frequencies ranging between 59 and 72 Hz in different sensors over parieto-occipital cortex contralateral to the side of stimulation. These oscillations showed maximal spectral amplitudes during the middle 200-300 ms of the delay phase and decreased again towards its end. Additionally, we investigated correlations between the activation strength of the gamma-band components and memory task performance. The magnitude of differentiation between oscillatory components representing 'preferred' and 'nonpreferred' stimuli during the final 100 ms of the delay phase correlated positively with task performance. These findings suggest that the observed gamma-band components reflect the activity of neuronal networks tuned to specific auditory spatial stimulus features. The activation of these networks seems to contribute to the maintenance of task-relevant information in short-term memory.Ergebnisse aus aktuellen Studien legen nahe, dass kortikale oszillatorische Aktivität im Gamma-Bereich (30-100 Hz) eine wichtige Rolle für verschiedene kognitive Prozesse spielt. Dazu zählen das Kodieren, die Aufrechterhaltung und der Abruf auditorischer, visueller oder taktiler Informationen in das bzw. aus dem Gedächtnis. Es konnte gezeigt werden, dass diese kortikale Aktivität durch Aufmerksamkeitsprozesse beeinflusst wird. Gamma-Aktivität trat bei vorangegangenen Untersuchungen als induzierte Antwort ca. 200-300 ms nach Stimuluspräsentation auf. Es wird angenommen, dass diese nicht phasengebundenen kortikalen Reizantworten aktive kortikale Verarbeitungs-prozesse widerspiegeln. In früheren Studien wurde induzierte Gamma-Aktivität während der Aufrechterhaltung von Stimulusinformationen über Regionen gefunden, die an der Verarbeitung aufgabenrelevanter Reizmerkmale beteiligt sind. Diese Antworten im Gamma-Bereich spiegelten Unterschiede zwischen verschieden experimentellen Bedingungen wider, jedoch ist wenig über die Repräsentation spezifischer Stimuluseigenschaften durch Gamma-Aktivität bekannt. Mit der vorliegenden Studie haben wir versucht, solche stimulus spezifischen Gamma-Komponenten zu untersuchen. Dafür verwendeten wir Magnetenzephalographie (MEG) und eine auditorische räumliche “delayed matching-to-sample“ Aufgabe. 28 gesunde Erwachsene wurden dabei zwei verschiedenen Gruppen zugeordnet. Gruppe R bekam rechtslateralisierte Stimuli präsentiert, während diese in Gruppe L linkslateralisiert waren. Dabei unterschieden sich die Reize nur in ihrer räumlichen Charakteristik, die Klangmuster blieben unverändert. In beiden Gruppen wurden zwei Beispielstimuli S1 mit Lateralisierungswinkeln von 15° bzw. 45° verwendet. Die Probanden mussten sich den Lateralisierungswinkel von S1 merken und anschließend mit einem zweiten Stimulus S2, der nach einer Verzögerungsphase von 800 ms präsentiert wurde, vergleichen. S2 hatte dabei entweder den gleichen Lateralisierungswinkel wie S1, oder unterschied sich darin von dem ersten Stimulus. Nach der Präsentation von S2 mussten die Probanden signalisieren, ob die Lateralisierungswinkel der beiden Stimuli übereinstimmten oder nicht. Die Signale der einzelnen Sensoren wurden mit einem statistischen Wahrscheinlichkeitsmapping untersucht. Dabei wollten wir Unterschiede in der spektralen Amplitude für Stimuli mit 15° bzw. 45° Lateralisierungswinkel identifizieren. Wir konnten spezifische Gamma-Aktivität für alle Beispielstimuli nachweisen. Die Signale wurden im Bereich von 59-72 Hz gefunden und waren über dem parieto-okzipitalen Kortex jeweils kontralateral zur stimulierten Seite lokalisiert. Die maximalen Spektralamplituden dieser Oszillationen traten während der mittleren 200-300 ms der Verzögerungsphase auf und nahmen zu ihrem Ende hin ab. Zusätzlich haben wir Korrelationen zwischen der Aktivierungsstärke der Gamma-Komponenten und dem Abschneiden bei der Gedächtnisaufgabe untersucht. Dabei zeigte sich, dass der Unterschied der oszillatorischen Antworten auf bevorzugte und nicht-bevorzugte Stimuli während der letzten 100 ms der Verzögerungsphase positiv mit der Leistung in der Gedächtnisaufgabe korrelierte. Diese Ergebnisse sprechen dafür, dass die beobachteten Gamma Komponenten die Aktivität neuronaler Netzwerke, die auf die Verarbeitung räumlicher auditorischer Information spezialisiert sind, widerspiegeln. Die Aktivierung dieser Netzwerke scheint zur Aufrechterhaltung aufgabenbezogener Information im Kurzzeitgedächtnis beizutragen

Frequency specific deficits in schizophrenia

Coherence estimation is one of the methods for understanding functional connectivity deficits and frequency specific deficits in schizophrenia. Coherence between different lobes of the brain from task related data at different frequency bands was investigated in patients with Schizophrenia (SP) and Healthy Normal Volunteers (HNV). The task was aimed to study the neural mechanisms underlying auditory and visual integration in patients with schizophrenia relative to healthy controls, which requires intact connectivity between the lobes of the brain in order to recombine the sensory information into a complete percept of the external world. Coherence was calculated from the processed magneto-encephalography (MEG) data for each pair of lobes of left temporal and parietal, left temporal and occipital, right temporal and parietal, right temporal and occipital, parietal and occipital at the frequency bands of delta (0 to 4 Hz), theta (4 -8 Hz), alpha (8-13 Hz), beta (13 -30 Hz) and gamma (30-100 Hz). Analysis Of Variance (ANOVA) was performed on the coherence data of 30 subjects comprised of 15 patients and 15 controls. There was a significant interaction between frequency and diagnosis with age. Significant differences were found between patients and controls at the Delta frequency band, which was confirmed with Bonferroni-corrected -t-tests at the delta frequency range in each pair of regions. It was found that patients had higher coherence than controls in the delta frequency band and it was significant across lobes which suggest an abnormal MEG coherence during evoked activity in schizophrenic patients

Dimension-reduction and discrimination of neuronal multi-channel signals

Dimensionsreduktion und Trennung neuronaler Multikanal-Signale