1,721,000 research outputs found
Introducing Local Weight Autocorrelation in Deep Neural Networks leads to the Emergence of Orientation Columns and Face Patches
No full textSmoothly varying orientation preference maps and category-selective regions are well-known prop-
erties of the primate visual system. With the aim of reproducing these properties in arti cial neural
network models of the ventral stream, we introduce the Hypercolumn layer, which is a subclass of the
locally-connected layer and an abstraction of the in
uential cortical Hypercolumn model. Our Hyper-
column layer minimizes the spatial distance of the layer weight vectors, which by de nition optimizes
a local weight autocorrelation. We show that deep neural networks using Hypercolumn layers produce
smooth orientation preference maps in shallow layers and category-selective regions in deeper layers when
trained on a categorical classi cation task. The strength of category selectivity is proportionate to the
degree of visual expertise that the model has with the category. We nd no substantial accuracy bene ts
of optimizing weight autocorrelation, although it reduces over tting. The biological implications of the
models are thoroughly discussed
Topographic Neural Networks show neural recycling of labile units during reading acquisition
No full textHere, we ask the question of how the formation of brain regions and changes in neuron-level specialisation
depend on environmental input changes by modelling neural recycling in a topographic
neural network. During reading acquisition, a region in the left hemisphere of the ventral visual
stream of the human brain emerges that is sensitive to written words. The emergence of this area,
the visual word form area, is strongly experience-dependent, but its predictable location points to an
underlying organisational principle that guides its emergence. It is thus hypothesised that it arises
due to ‘recycling’ of neurons that were previously not or weakly involved in object recognition. To
uncover more about the spatial changes during the formation of functionally specialised areas in the
brain, we use use a topographic neural network (TNN) to model neural recycling. To simulate reading
acquisition, we first train the network on a large-scale dataset of natural images (preliterate phase).
Next, the network is trained for 50 epochs more on word images as well (literate phase). We confirm
recycling of ‘labile’ units (non-selective to any category) and face-selective units to word-selective
units after reading acquisition. Word-selective units cluster together, especially in the later layers of
the network. We also confirm the destructive e↵ect of neural recycling on the performance on other
classes of stimuli. We conclude that the TNN serves well as a model of neural recycling, as it captures
various features of neural recycling in the human visual cortex
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
From visual representations to perceptual decisions: investigating neural mechanisms underlying visual recognition and perceptual decision making
Full text linkTo successfully interact with the environment, humans must robustly encode sensory inputs into neural representations and translate these representations into adaptive behavior.
In this thesis, I conducted three empirical studies targeting particular neural mechanisms
entailed in this transition from sensory inputs to perceptual decisions.
In Study 1, I probed the robustness of core visual processing mechanisms by characterizing and comparing the neural dynamics of object recognition for natural photographs
and abstracted line drawings. This revealed that core visual processing mechanisms in the
brain are robust to the abstraction of substantial amounts of visual information, such as in
line drawings.
In Study 2, I investigated the influence of feedback processing on object representations by
comparing the neural dynamics of object recognition for stimuli that were either rapidly
followed by a masking stimulus or followed only after a substantial delay. This revealed
that feedback processing fundamentally shapes visual representations in the brain, first
in early than in high-level visual cortex. Feedback enhances the persistence of visual
representations, causes a shift in the representational format in high-level visual cortex,
and affects distinct spectro-temporal windows in the theta to beta frequency bands.
Finally, in Study 3, I examined the link between neural representations of real-world scenes
and behavior under varying task demands. The findings showed that distinct visual representations in the brain are behaviorally relevant depending on the task, that mid-level
visual features underlie these behaviorally relevant representations, and that visual representations can interfere with behavior given task demands that do not align with the
represented information.
By demonstrating the robustness of core visual processing mechanisms to visual abstractions and by characterizing how feedback processing dynamically shapes visual processing, the findings in Study 1 and 2 provide complimentary insights into the neural mechanisms that enable robust encoding of visual information. By identifying and characterizing visual representations relevant for behavior across different task demands, Study 3 provides novel insights into the translation of sensory information into perceptual decisions. Collectively, these results contribute to a large body of research on visual recognition and perceptual decision making, provide potential new theoretical frameworks for understanding the underlying mechanisms, and guide
the way for future research that directly tests and refines these theories.Um erfolgreich mit der Umwelt zu interagieren, müssen Menschen sensorische Reize robust enkodieren und die resultierenden Repräsentationen in Verhalten umwandeln. In
dieser Dissertation habe ich drei empirischen Studie durchgeführt, um spezifische neuronale Mechanismen zu untersuchen, die diesem Übergang von sensorischen Reizen zu
Wahrnehmungsentscheidungen zugrunde liegen.
In Studie 1 habe ich die Robustheit grundlegender visueller Verarbeitungsmechanismen untersucht, indem ich die neuronale Dynamik der Objekterkennung für natürliche Fotografien und abstrahierte Strichzeichnungen charakterisiert und verglichen habe. Die
Ergebnisse zeigten, dass grundlegende visuelle Verarbeitungsmechanismen robust gegenüber der Abstraktion großer Mengen visueller Informationen sind, wie es in Strichzeichnungen der Fall ist.
In Studie 2 untersuchte ich den Einfluss von Feedbackverarbeitung auf Objektrepräsentationen, indem ich die neuronale Dynamik der Objekterkennung für Stimuli verglich, die
entweder schnell von einem Maskierungsreiz gefolgt wurden oder erst nach einer längeren
Verzögerung. Die Ergebnisse zeigten, dass Feedback die visuellen Repräsentationen im
Gehirn grundlegend beeinflusst, zunächst im frühen und später im höheren visuellen Kortex. Feedback erhöht die Beständigkeit visueller Repräsentationen, führt zu einer Veränderung im Repräsentationsformat im höheren visuellen Kortex und beeinflusst spezifische
spektral-temporale Fenster in den Theta- bis Beta-Frequenzbändern.
Abschließend habe ich in Studie 3 den Zusammenhang zwischen neuronalen Repräsentationen realer Szenenbilder und dem Verhalten während verschiedener Aufgaben untersucht. Die Ergebnisse zeigten, dass unterschiedliche visuelle Repräsentationen im Gehirn
je nach Aufgabe verhaltensrelevant sind, dass visuelle Informationen mittlerer Komplexität diesen Repräsentationen zugrunde liegen und dass visuelle Repräsentationen mit Verhalten interferieren können, wenn die Aufgabenanforderungen nicht mit der repräsentierten Information übereinstimmen.
Durch die Demonstration der Robustheit grundlegender visueller Verarbeitunsgsmechanismen gegenüber visueller Abstraktion und die Charakterisierung, wie Feedback visuelle Verarbeitung dynamisch formt, liefern die Ergebnisse aus Studie 1 und 2 komplementäre Einblicke in die neuronalen Mechanismen, die die robuste Enkodierung visueller Informationen ermöglichen. Durch die Identifizierung und Charakterisierung verhaltensrelevanter visueller Repräsentationen, liefert Studie 3 neue Erkenntnisse über die
Umwandlung von sensorischen Reizen in Wahrnehmungsentscheidungen. Insgesamt tragen diese Ergebnisse zu einem breiten Forschungsfeld zu visueller Objekterkennung und Wahrnehmungsentscheidungen bei, liefern neue theoretische Anhaltspunkte für das Verständnis der zugrundeliegenden neuronalen Mechanismen und können zukünftige Forschung
anleiten, die diese Theorien direkt testet
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Implementing Fixational Eye Movement in a Recurrent Neural Network using Reinforcement Learning to achieve Super Resolution
No full textMy experiment explored Fixational Eye Movements' benefits in a Classification task with input with partially destroyed information. Rucci et al. findings suggest that FEMs play a crucial role in visual perception, especially when it comes to high-frequency data. Only a small part of the retina called the foveola can capture the full details of high-frequency input for many organisms like humans. To see in high-resolution with this physical limitation, we have to efficiently shift the input over this sensitive area. This unconscious process is called FEMs. In my experiment, I succeeded in learning an RNN the benefits of FEMs via Reinforcement learning. I could replicate some of Rucci et al. results and find evidence that FEM's are not just a useless bug
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