Search Results - "Sandamirskaya, Yulia"

An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot by Kreiser, Raphaela, Renner, Alpha, Leite, Vanessa R. C., Serhan, Baris, Bartolozzi, Chiara, Glover, Arren, Sandamirskaya, Yulia

“…In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal…”
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Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System by Milde, Moritz B, Blum, Hermann, Dietmüller, Alexander, Sumislawska, Dora, Conradt, Jörg, Indiveri, Giacomo, Sandamirskaya, Yulia

“…Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture…”
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Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement by Tekülve, Jan, Fois, Adrien, Sandamirskaya, Yulia, Schöner, Gregor

“…Neurally inspired robotics already has a long history that includes reactive systems emulating reflexes, neural oscillators to generate movement patterns, and…”
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Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields by Kreiser, Raphaela, Aathmani, Dora, Qiao, Ning, Indiveri, Giacomo, Sandamirskaya, Yulia

“…Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal…”
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Using Dynamic Field Theory to extend the embodiment stance toward higher cognition by Sandamirskaya, Yulia, Zibner, Stephan K.U., Schneegans, Sebastian, Schöner, Gregor

“…The embodiment stance emphasizes that cognitive processes unfold continuously in time, are constantly linked to the sensory and motor surfaces, and adapt…”
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Dynamic Neural Fields with Intrinsic Plasticity by Strub, Claudius, Schöner, Gregor, Wörgötter, Florentin, Sandamirskaya, Yulia

“…Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based…”
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Dynamic neural fields as a step toward cognitive neuromorphic architectures by Sandamirskaya, Yulia

“…Dynamic Field Theory (DFT) is an established framework for modeling embodied cognition. In DFT, elementary cognitive functions such as memory formation,…”
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Parsing of action sequences: A neural dynamics approach by Lobato, David, Sandamirskaya, Yulia, Richter, Mathis, Schöner, Gregor

“…Parsing of action sequences is the process of segmenting observed behavior into individual actions. In robotics, this process is critical for imitation…”
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Using haptics to extract object shape from rotational manipulations by Strub, Claudius, Worgotter, Florentin, Ritter, Helge, Sandamirskaya, Yulia

“…Increasingly widespread available haptic sensors mounted on articulated hands offer new sensory channels that can complement shape extraction from vision to…”
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Learning the Condition of Satisfaction of an Elementary Behavior in Dynamic Field Theory by Luciw, Matthew, Kazerounian, Sohrob, Lahkman, Konstantin, Richter, Mathis, Sandamirskaya, Yulia

“…In order to proceed along an action sequence, an autonomous agent has to recognize that the intended final condition of the previous action has been achieved…”
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Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance Targeting Neuromorphic Processors by Salt, Llewyn, Howard, David, Indiveri, Giacomo, Sandamirskaya, Yulia

“…The Lobula giant movement detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses…”
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Effective and natural human-robot interaction requires multidisciplinary research by Kragic, Danica, Sandamirskaya, Yulia

“…This special issue highlights the interdisciplinary nature of the research we conduct toward the development of HRI…”
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Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook by Davies, Mike, Wild, Andreas, Orchard, Garrick, Sandamirskaya, Yulia, Guerra, Gabriel A. Fonseca, Joshi, Prasad, Plank, Philipp, Risbud, Sumedh R.

“…Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem…”
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Learning Temporal Intervals in Neural Dynamics by Duran, Boris, Sandamirskaya, Yulia

“…Storing and reproducing temporal intervals is an important component of perception, action generation, and learning. How temporal intervals can be represented…”
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Learning and adaptation: neural and behavioural mechanisms behind behaviour change by Lowe, Robert, Sandamirskaya, Yulia

“…This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and…”
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Learning and Adaptation: Neuronal Mechanisms Behind Behaviour Change by Lowe, Robert, Sandamirskaya, Yulia

“…This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and…”
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Report about the 35th annual meeting of the cognitive science society by Sandamirskaya, Yulia

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Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM by Kreiser, Raphaela, Renner, Alpha, Sandamirskaya, Yulia, Pienroj, Panin

“…In this paper, we investigate the use of ultra low-power, mixed signal analog/digital neuromorphic hardware for implementation of biologically inspired…”
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An embodied account of serial order: How instabilities drive sequence generation by Sandamirskaya, Yulia, Schöner, Gregor

“…Learning and generating serially ordered sequences of actions is a core component of cognition both in organisms and in artificial cognitive systems. When…”
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Event-based PID controller fully realized in neuromorphic hardware: a one DoF study by Stagsted, Rasmus Karnoe, Vitale, Antonio, Renner, Alpha, Larsen, Leon Bonde, Christensen, Anders Lyhne, Sandamirskaya, Yulia

“…Spiking Neuronal Networks (SNNs) realized in neuromorphic hardware lead to low-power and low-latency neuronal computing architectures. Neuromorphic computing…”
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