Student Projects

Text Embeddings Optimized for Distance Computations (MSc Thesis)

The goal of this project is to develop new methods for representation learning of documents and sentences, that are trained to approximate the distance between two documents.

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Intrinsic Optical Imaging for the Mapping of Cortical Responses

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Focused Ultrasound mediated Drug delivery: In-Vitro Tests

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Online Monitoring of Vital Signs & Anesthesia during Neural Recordings

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Classification of Radio Signals on a neuromorphic processor in Space

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Fabrication of flexible microelectrode arrays used for brain machine interfaces

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Fully automated extraction of neural signals from imaging data

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Machine learning for neuronal activity

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New projects from devices to systems with the Sensors group

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Controlling sequential movements with neural networks in neuromorphic hardware

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Learning experience maps on a neuromorphic chip with Khepera robot equipped with a dynamic vision sensor

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Neural network implementation in neuromorphic hardware for unsupervised learning of MNIST digits

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Syrinx's biomechanics in songbirds with in vivo high-speed 2D tomograms (PSI)

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Computer vision based reconstruction of neuromorphological features in the songbird’s syrinx on 3D tomograms (PSI)

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Reinforcement learning of human vocal behavior

We study reinforcement learning of fundamental frequency (pitch) in songbirds and humans. When birds receive aversive reinforcement for low-pitch syllables they successfully learn to increase the syllables’ pitch.

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Psychophysical Theory of Human Pitch Processing

We study the mechanisms of fundamental frequency (pitch) adaptation of songbird and human vocalizations. Adaptation can be induced as a response to distortions of pitch feedback.

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The role of successful and unsuccessful trials during motor learning

Reinforcement (‘good dog’, ‘bad dog’) is one of the main strategies to train animals and humans. We have used this strategy extensively in the lab to train songbirds to change their song and study the neural correlates of vocal and motor learning.

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Analysis of birdsong development and automated clustering of song syllables

During early development, young songbirds such as the Zebra Finch learn acoustically complex but stereotyped sequential behaviors which are termed "songs". Furthermore, zebra finches learn only one song in their lifetime, making the problem of developmental song analysis tractable.

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Neuronal controllers for cognitive robots

In our group “Neuromorphic Cognitive Systems", we develop neuronal architectures that allow robots to generate behavior (e.g. navigate in an environment, avoid obstacles, pursue targets), to form memories (e.g. build a map), and learn.

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Real-time feedback-controlled delivery of neuromodulators for non-invasive high-resolution modulation of brain

We are employing MRI guided focused ultrasound based delivery of neuromodulators to control activity of specific brain micro circuits and subsequent cognitive behavior, which has both fundamental and medical applications.

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Deep neural networks for auditory scene analysis

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A Spike based ADC for calcium bio-signal recording in the mouse brain

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Depth vision with a neuromorphic sensor on a robotic vehicle

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Neural underpinnings of auditory observational learning in Zebra Finches

Learning by observation is an important decision making strategy. We examined this in songbirds by pairing zebra finches and training one finch (demonstrator) to discriminate song syllables while the other (observer) observes this behavior. We show that observer finches acquire this behavior faster.

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Neuromorphic controller for an autonomous quadcopter

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