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To fully comprehend visual perception, we need to necessarily understand its temporal dimension. Our visual environment is highly dynamic, requiring the processing and integration of temporal signals in order to make sense of it. Many processes, such as th ...
Sensory stimuli have long been thought to be represented in the brain as activity patterns of specific neuronal assemblies. However, we still know relatively little about the long-term dynamics of sensory representations. Using chronic in vivo calcium imag ...
Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style.
The main topic of this study is grammar induction for harmony --- the process of learning structural principles f ...
Empirical research on the bodily self has shown that the body representation is malleable, and prone to manipulation when conflicting sensory stimuli are presented. Using Virtual Reality (VR) we assessed the effects of manipulating multisensory feedback (f ...
Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...
Massed synchronised neuronal firing is detrimental to information processing. When networks of task-irrelevant neurons fire in unison, they mask the signal generated by task-critical neurons. On a macroscopic level, such synchronisation can contribute to a ...
Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception were the focus of the present study. We presented 250 ms duration sounds of living and man-made objects ...
The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM- -based algorithm, capable of learning model representations incrementally while keeping under ...
A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...
The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under c ...
