Über die Selbstorganisation einer hierarchischen Gedächtnisstruktur für kompositionelle Objektrepräsentation im visuellen Kortex

At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.Gegenwärtig besteht immer noch ein enormer Abstand zwischen der Lernfähigkeit von künstlichen und biologischen Informationsverarbeitungssystemen. Dieser Abstand ließe sich durch eine bessere Einsicht in die höheren Funktionen des Gehirns wie Lernen und Gedächtnis verringern. Im visuellen Kortex etwa werden die Objekte innerhalb kürzester Zeit entlang der hierarchischen Verarbeitungspfade in ihre Bestandteile zerlegt und so durch eine Komposition von Elementen niedrigerer Komplexität dargestellt. Bereits bekannte Objekte werden so aus dem Langzeitgedächtnis abgerufen und wiedererkannt. Wie eine derartige kompositionell-hierarchische Gedächtnisstruktur durch die visuelle Erfahrung zustande kommen kann, ist noch weitgehend ungeklärt. Um dieser Frage nachzugehen, wird hier ein funktionelles Modell eines lernfähigen rekurrenten neuronalen Netzwerkes vorgestellt. Im Netzwerk werden neuronale Mechanismen implementiert, die der kortikalen Verarbeitung und Plastizität zugrunde liegen. Die hierarchische Architektur des Netzwerkes besteht aus zwei nacheinander geschalteten Schichten, die jede eine Anzahl von verteilten, rekurrent vernetzten Modulen beherbergen. Ein Modul umfasst dabei mehrere funktionell separate Subnetzwerke. Jedes solches Modul ist imstande, aus den eintreffenden Signalen eine geeignete Repräsentation für den lokalen Eingaberaum unüberwacht zu lernen. Die fortlaufende Verarbeitung im Netzwerk setzt sich zusammen aus diskreten Fragmenten, genannt Entscheidungszyklen, die man mit den schnellen kortikalen Rhythmen im gamma-Frequenzbereich in Verbindung setzen kann. Die Zyklen sind synchronisiert zwischen den verteilten Modulen. Innerhalb eines Zyklus wird eine lokal umgrenzte winner-take-all-ähnliche Operation in Modulen durchgeführt. Die Kompetitionsstärke wächst im Laufe des Zyklus an. Diese Operation aktiviert in Abhängigkeit von den Eingabesignalen eine sehr kleine Anzahl von Einheiten und verstärkt sie auf Kosten der anderen, um den dargebotenen Reiz in der Netzwerkaktivität abzubilden. Ausgestattet mit adaptiven Mechanismen der bidirektionalen synaptischen Plastizität und der homöostatischen Aktivitätsregulierung, erhält das Netzwerk natürliche Gesichtsbilder von verschiedenen Personen dargeboten. Die Bilder werden der unteren Netzwerkschicht, je ein Bild pro Zyklus, als Ansammlung von Gaborfilterantworten aus lokalen Gesichtslandmarken zugeführt, ohne Information über die Personenidentität zur Verfügung zu stellen. Im Laufe der unüberwachten Lernprozedur formt das Netzwerk die Verbindungsstruktur derart, dass die Gesichter aller dargebotenen Personen im Netzwerk in Form von dünn besiedelten Gedächtnisspuren abgelegt werden. Hierzu werden gleichzeitig vorwärtsgerichtete (bottom-up) und rekurrente (lateral, top-down) synaptische Verbindungen innerhalb und zwischen den Schichten gelernt. Im reifen Verbindungszustand werden infolge dieses Lernens die einzelnen Gesichter als Komposition ihrer Bestandteile auf generative Art gespeichert. Dank der generativen Art der gelernten Struktur reichen schon allein das höhere Identitätssymbol oder eine kleine Teilmenge von zugehörigen Gesichtselementen, um alle Bestandteile der gespeicherten Gesichter aus dem Gedächtnis abzurufen. In der Testphase kann das Netzwerk erfolgreich sowohl die Identität als auch das Geschlecht von Personen aus vorher nicht gezeigten Gesichtsansichten erkennen. Eine bemerkenswerte Eigenschaft der entstandenen Gedächtnisarchitektur ist ihre Fähigkeit, ohne Darbietung von externen Stimuli spontan Aktivitätsmuster zu generieren und die im Gedächtnis abgelegten Inhalte in diesem schlafähnlichen "off-line" Regime wiederzugeben. Interessanterweise ergibt sich aus der Schlafphase ein direkter Vorteil für die Gedächtnisfunktion. Dieser Vorteil macht sich durch eine drastisch verbesserte Erkennungsrate nach der Schlafphase bemerkbar, wenn das Netwerk mit den zuvor nicht dargebotenen Ansichten von den bereits bekannten Personen konfrontiert wird. Die Leistungsverbesserung nach der Schlafphase ist umso deutlicher, je stärker die Alternativansichten vom Original abweichen. Dieser positive Effekt ist zudem komplett unabhängig von der synapsenspezifischen Plastizität und kann allein durch die synapsenunspezifische, homöostatische Regulation der Aktivität im Netzwerk erklärt werden. Das entwickelte Netzwerk demonstriert so eine im Bereich der neuronalen Modellierung bisher nicht gezeigte Funktionalität. Es kann unüberwacht eine Gedächtnisdomäne für kompositionelle, generative Objektrepräsentation durch die Erfahrung mit natürlichen Bildern sowohl im reizgetriebenen, wachähnlichen Zustand als auch im reizabgekoppelten, schlafähnlichen Zustand formen und verwalten. Diese Funktionalität bietet einen vielversprechenden Ausgangspunkt für weitere Studien, die die neuronalen Lernmechanismen des Gehirns ins Visier nehmen und letztendlich deren konsequente Umsetzung in technischen, adaptiven Systemen anstreben

A neural network model of normal and abnormal learning and memory consolidation

The amygdala and hippocampus interact with thalamocortical systems to regulate cognitive-emotional learning, and lesions of amygdala, hippocampus, thalamus, and cortex have different effects depending on the phase of learning when they occur. In examining eyeblink conditioning data, several questions arise: Why is the hippocampus needed for trace conditioning where there is a temporal gap between the conditioned stimulus offset and the onset of the unconditioned stimulus, but not needed for delay conditioning where stimuli temporally overlap and co-terminate? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later have no impact on conditioned behavior? Why do thalamic lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions degrade recent learning but not temporally remote learning? Why do cortical lesions degrade temporally remote learning, and cause amnesia, but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of medial prefrontal cortex? How are mechanisms of motivated attention and the emergent state of consciousness linked during conditioning? How do neurotrophins, notably Brain Derived Neurotrophic Factor (BDNF), influence memory formation and consolidation? A neural model, called neurotrophic START, or nSTART, proposes answers to these questions. The nSTART model synthesizes and extends key principles, mechanisms, and properties of three previously published brain models of normal behavior. These three models describe aspects of how the brain can learn to categorize objects and events in the world; how the brain can learn the emotional meanings of such events, notably rewarding and punishing events, through cognitive-emotional interactions; and how the brain can learn to adaptively time attention paid to motivationally important events, and when to respond to these events, in a context-appropriate manner. The model clarifies how hippocampal adaptive timing mechanisms and BDNF may bridge the gap between stimuli during trace conditioning and thereby allow thalamocortical and corticocortical learning to take place and be consolidated. The simulated data arise as emergent properties of several brain regions interacting together. The model overcomes problems of alternative memory models, notably models wherein memories that are initially stored in hippocampus move to the neocortex during consolidation

A neural network model of normal and abnormal learning and memory consolidation

The amygdala and hippocampus interact with thalamocortical systems to regulate cognitive-emotional learning, and lesions of amygdala, hippocampus, thalamus, and cortex have different effects depending on the phase of learning when they occur. In examining eyeblink conditioning data, several questions arise: Why is the hippocampus needed for trace conditioning where there is a temporal gap between the conditioned stimulus offset and the onset of the unconditioned stimulus, but not needed for delay conditioning where stimuli temporally overlap and co-terminate? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later have no impact on conditioned behavior? Why do thalamic lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions degrade recent learning but not temporally remote learning? Why do cortical lesions degrade temporally remote learning, and cause amnesia, but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of medial prefrontal cortex? How are mechanisms of motivated attention and the emergent state of consciousness linked during conditioning? How do neurotrophins, notably Brain Derived Neurotrophic Factor (BDNF), influence memory formation and consolidation? A neural model, called neurotrophic START, or nSTART, proposes answers to these questions. The nSTART model synthesizes and extends key principles, mechanisms, and properties of three previously published brain models of normal behavior. These three models describe aspects of how the brain can learn to categorize objects and events in the world; how the brain can learn the emotional meanings of such events, notably rewarding and punishing events, through cognitive-emotional interactions; and how the brain can learn to adaptively time attention paid to motivationally important events, and when to respond to these events, in a context-appropriate manner. The model clarifies how hippocampal adaptive timing mechanisms and BDNF may bridge the gap between stimuli during trace conditioning and thereby allow thalamocortical and corticocortical learning to take place and be consolidated. The simulated data arise as emergent properties of several brain regions interacting together. The model overcomes problems of alternative memory models, notably models wherein memories that are initially stored in hippocampus move to the neocortex during consolidation

Unsupervised Automatic Detection Of Transient Phenomena In InSAR Time-Series using Machine Learning

The detection and measurement of transient episodes of crustal deformation from global InSAR datasets are crucial for a wide range of solid earth and natural hazard applications. But the large volumes of unlabelled data captured by satellites preclude manual systematic analysis, and the small signal-to-noise ratio makes the task difficult. In this thesis, I present a state-of-the-art, unsupervised and event-agnostic deep-learning based approach for the automatic identification of transient deformation events in noisy time-series of unwrapped InSAR images. I adopt an anomaly detection framework that learns the ‘normal’ spatio-temporal pattern of noise in the data, and which therefore identifies any transient deformation phenomena that deviate from this pattern as ‘anomalies’. The deep-learning model is built around a bespoke autoencoder that includes convolutional and LSTM layers, as well as a neural network which acts as a bridge between the encoder and decoder. I train our model on real InSAR data from northern Turkey and find it has an overall accuracy and true positive rate of around 85% when trying to detect synthetic deformation signals of length-scale > 350 m and magnitude > 4 cm. Furthermore, I also show the method can detect (1) a real Mw 5.7 earthquake in InSAR data from an entirely different region- SW Turkey, (2) a volcanic deformation in Domuyo, Argentina, (3) a synthetic slow-slip event and (4) an interseismic deformation around NAF in a descending frame in northern Turkey. Overall I show that my method is suitable for automated analysis of large, global InSAR datasets, and for robust detection and separation of deformation signals from nuisance signals in InSAR data

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Efficient Semantic Segmentation for Resource-Constrained Applications with Lightweight Neural Networks

This thesis focuses on developing lightweight semantic segmentation models tailored for resource-constrained applications, effectively balancing accuracy and computational efficiency. It introduces several novel concepts, including knowledge sharing, dense bottleneck, and feature re-usability, which enhance the feature hierarchy by capturing fine-grained details, long-range dependencies, and diverse geometrical objects within the scene. To achieve precise object localization and improved semantic representations in real-time environments, the thesis introduces multi-stage feature aggregation, feature scaling, and hybrid-path attention methods

FIAS Scientific Report 2011

In the year 2010 the Frankfurt Institute for Advanced Studies has successfully continued to follow its agenda to pursue theoretical research in the natural sciences. As stipulated in its charter, FIAS closely collaborates with extramural research institutions, like the Max Planck Institute for Brain Research in Frankfurt and the GSI Helmholtz Center for Heavy Ion Research, Darmstadt and with research groups at the science departments of Goethe University. The institute also engages in the training of young researchers and the education of doctoral students. This Annual Report documents how these goals have been pursued in the year 2010. Notable events in the scientific life of the Institute will be presented, e.g., teaching activities in the framework of the Frankfurt International Graduate School for Science (FIGSS), colloquium schedules, conferences organized by FIAS, and a full bibliography of publications by authors affiliated with FIAS. The main part of the Report consists of short one-page summaries describing the scientific progress reached in individual research projects in the year 2010..

Neurodemocracy: Self-Organization of the Embodied Mind

This thesis contributes to a better conceptual understanding of how self-organized control works. I begin by analyzing the control problem and its solution space. I argue that the two prominent solutions offered by classical cognitive science (centralized control with rich commands, e.g., the Fodorian central systems) and embodied cognitive science (distributed control with simple commands, such as the subsumption architecture by Rodney Brooks) are merely two positions in a two-dimensional solution space. I outline two alternative positions: one is distributed control with rich commands, defended by proponents of massive modularity hypothesis; the other is centralized control with simple commands. My goal is to develop a hybrid account that combines aspects of the second alternative position and that of the embodied cognitive science (i.e., centralized and distributed controls with simple commands). Before developing my account, I discuss the virtues and challenges of the first three. This discussion results in a set of criteria for successful neural control mechanisms. Then, I develop my account through analyzing neuroscientific models of decision-making and control with the theoretical lenses provided by formal decision and social choice theories. I contend that neural processes can be productively modeled as a collective of agents, and neural self-organization is analogous to democratic self-governance. In particular, I show that the basal ganglia, a set of subcortical structures, contribute to the production of coherent and intelligent behaviors through implementing “democratic" procedures. Unlike the Fodorian central system—which is a micro-managing “neural commander-in-chief”—the basal ganglia are a “central election commission.” They delegate control of habitual behaviors to other distributed control mechanisms. Yet, when novel problems arise, they engage and determine the result on the basis of simple information (the votes) from across the system with the principles of neurodemocracy, and control with simple commands of inhibition and disinhibition. By actively managing and taking advantage of the wisdom-of-the-crowd effect, these democratic processes enhance the intelligence and coherence of the mind’s final "collective" decisions. I end by defending this account from both philosophical and empirical criticisms and showing that it meets the criteria for successful solution