Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

Synchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

Stimulus and task-dependent gamma activity in monkey V1

The single unit doctrine proposes that each one of our percepts and sensations is represented by the activity of specialized high-level cells in the brain. A common criticism applied to this proposal is the one referred to as the "combinatorial problem". We are constantly confronted with unlimited combinations of elements and features, and yet we face no problem in recognizing patterns and objects present in visual scenes. Are there enough neurons in the brain to singly code for each one of our percepts? Or is it the case that perceptions are represented by the distributed activity of different neuronal ensembles? We lack a general theory capable of explaining how distributed information can be efficiently integrated into single percepts. The working hypothesis here is that distributed neuronal ensembles signal relations present in the stimulus by selectively synchronizing their spiking responses. Synchronization is generally associated with oscillatory activity in the brain. Gamma oscillations in particular have been linked to various integrative processes in the visual system. Studies in anesthetized animals have shown a conspicuous increase in power for the gamma frequency band (30 to 60 Hz) in response to visual stimuli. Recently, these observations have been extended to behavioral studies which addressed the role of gamma activity in cognitive processes demanding selective attention. The initial motivation for carrying out this work was to test if the binding-by-synchronization (BBS) hypothesis serves as a neuronal mechanism for perceptual grouping in the visual system. To this aim we used single and superimposed grating stimuli. Superimposed gratings (plaids) are bi-stable stimuli capable of eliciting different percepts depending on their physical characteristics. In this way, plaids can be perceived either as a single moving surface (pattern plaids), or as two segregated surfaces drifting in different directions (component plaids). While testing the BBS hypothesis, we performed various experiments which addressed the role of both stimulus and cortical architecture on the properties of gamma oscillations in the primary visual cortex (V1) of monkeys. Additionally, we investigated whether gamma activity could also be modulated by allocating attention in time. Finally, we report on gamma-phase shifts in area V1, and how they depend on the level of neuronal activation. ...Einleitung: Die visuelle Hirnforschung hat eine große Informationsmenge über die analytischen Fähigkeiten des Nervensystems zusammengetragen. Die Einführung von Einzelzellableitungen ermöglichte eine detaillierte Beschreibung der Eigenschaften rezeptiver Felder im Sehsystem. Konzentrische rezeptive Felder in der Netzhaut antworten optimal auf einen Luminanzkontrast in ihren On- und Off-Regionen. Antworteigenschaften entwickeln sich schrittweise entlang der Sehbahn, indem zunehmend komplexere Eigenschaften des visuellen Reizes extrahiert werden. Die Pionierarbeiten von David Hubel und Torsten Wiesel beschrieben zunächst Orientierung- und Richtungsselektivität von Neuronen in frühen visuellen Kortexarealen. Später fand man Einzelzellen im medialen Temporallappen, die auf komplexe Objekte wie Hände und Gesichter antworten. Die Hirnforschung ist daher lange davon ausgegangen, dass die Repräsentation komplexer Objekte eine natürliche Entfaltung von Konvergenz entlang der Sehbahn darstellt. Zellen, welche auf elementare Merkmale des Stimulus antworteten, bildeten so durch ihr Muster anatomischer Verbindungen schrittweise die spezialisierten Neurone in höheren visuellen Arealen. Diese Sichtweise zeigt allerdings Limitationen auf. Eine beständige Kritik, die an der "Einzelzelldoktrin" geübt wird, ist das sogenannte kombinatorische Problem. Obwohl wir ständig mit einer unbegrenzten Fülle an Kombinationen verschiedener Elemente und Merkmale konfrontiert sind, laufen wir selten Gefahr, Muster und Objekte in einer visuellen Szene nicht zu erkennen. Ist es überhaupt möglich, dass jedes unserer möglichen Perzepte durch die Antwort eines einzelnen hoch spezialisierten Neurons im Hirn kodiert wird? Falls nicht, welcher Mechanismus könnte einen relationalen Code darstellen, der es ermöglicht, die Aktivität verschiedener neuronaler Ensembles zu integrieren? Die Anforderungen an einen solchen Mechanismus treten besonders hervor, wenn man sich die verteilte Struktur der visuellen Verarbeitung verdeutlicht. Die Merkmalsextraktion entlang der Sehbahn führt unvermeidbar zu einer räumlich verstreuten Repräsentation eines visuellen Reizes. Zusätzlich kommen parallele Bahnen neuronaler Verarbeitung im Hirn häufig vor. Es fehlt eine universale Theorie darüber, wie die verteilte Information effizient in eine einzige Wahrnehmung integriert wird. Die Arbeitshypothese hier lautet, dass das Hirn die Zeitdomäne benutzt, um visuelle Informationen zu integrieren und zu verarbeiten. Konkret würden neuronale Ensemble die aus dem Stimulus hervorgehenden Beziehungen durch eine selektive Synchronisation ihrer Aktionspotenziale signalisieren. Synchronisation ist normalerweise mit oszillatorischer Hirnaktivität assoziiert. Besonders die Oszillationen im Gamma Frequenzband sind mit verschiedensten integrativen Prozessen im Sehsystem in Verbindung gebracht worden. Arbeiten an anästhesierten Tieren haben einen auffälligen Anstieg von Energie im Gamma Frequenzband (30-60 Hz) unter visueller Stimulation gezeigt. Kürzlich sind diese Beobachtungen auf Verhaltensstudien ausgeweitet worden, welche die Rolle von Gamma Aktivität bei der für kognitive Prozesse erforderlichen gerichteten Aufmerksamkeit untersuchen. Die ursprüngliche Motivation dieser Arbeit war es, die von Wolf Singer und Mitarbeitern formulierte "binding-bysynchronization (BBS)" Hypothese zu testen. Dies wurde durch die Ableitung neuronaler Antworten in V1 bei Darbietung eines Paars übereinander gelegter Balkengitter ("Plaid" Stimulus) angegangen. Physikalische Manipulationen der Luminanz in Unterregionen des Plaid-Stimulus können die Wahrnehmung zugunsten der Bewegung der Einzelkomponenten (zwei Objekte, die sich übereinander schieben) oder der Bewegung des Gesamtmusters (ein einziges sich in eine gemeinsame Richtung bewegendes Objekt) beeinflussen. Die gleichzeitige Ableitung von zwei Neuronen, die jeweils nur selektiv auf eines der beiden Balkengitter antworteten, ermöglichte es uns, zwei Vorhersagen der BBS Hypothese zu testen. Falls beide V1 Neurone auf dasselbe Balkengitter antworteten, sollten sie ihre Aktivität unabhängig davon, ob das Plaid in Einzelkomponenten oder als Gesamtmuster wahrgenommen würde, synchronisieren. Der Grund dafür wäre, dass beide Neurone auf dasselbe Objekt reagierten. Im zweiten Fall antworten beide Ableitstellen auf jeweils eine der beiden Balkengitterkomponenten. Hier sagt die BBS Hypothese voraus, dass beide ihre Aktivität nur bei Gesamtmusterbewegung synchronisieren würden, da sie nur in dieser Bedingung auf dasselbe Objekt antworten würden. ..

The What and Why of Binding: The Modeler's Perspective

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call "binding." Binding by neural signal synchrony had been mentioned several times in the liter ature (Lege´ndy, 1970; Milner, 1974) before it was fully formulated as a general phenomenon (von der Malsburg, 1981). Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it

Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the-art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units