 Institute of NeuroinformaticsOpen OpportunitiesOur research group aims to enhance the understanding of human language acquisition and development using songbird as model.
We are particularly interested in the evolutionary aspects of language, where two developmental tendencies are observed: convergent and divergent evolution. Convergent evolution refers to the simplification of language complexity, similar to how infants gradually acquire human language. Conversely, divergent evolution involves an increase in complexity, akin to teenagers creating and using novel words to establish unique identities. We propose to investigate whether similar effects are observable in animal vocalization learning, specifically in song learning of zebra finches and to explore the effect of social interaction.
To facilitate this investigation, our team has developed a "birdpark," a multimodal recording system that provides a naturalistic social environment for observing and recording multiple zebra finches within a dynamic group context.
- Learning, Memory, Cognition and Language, Linguistic Processes (incl. Speech Production and Comprehension), Sensory Systems, Signal Processing, Zoology
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| This project aims to classify objects of various shapes using tactile data from a 32x32 sensor array. The dataset includes shapes with varying sides, sizes, locations, trace speeds, and widths. A spiking neural network (SNN) implemented on the neuromorphic Dynapse chip will process the tactile data to spatially reproduce object shapes on-chip, enabling classification and clustering of tactile patterns. The system is designed to recognize shapes independent of factors like size and trace speed, leveraging the event-driven architecture of the Dynapse chip, which mimics biological neurons and synapses for efficient real-time processing of spatiotemporal data. - Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Course Project, Master Thesis, Semester Project
| Developing a state-machine Simulink model to be deployed at MathWorks SpeedGoat real-time target machine for closed-loop brain-machine interface (BMI). The state-machine will control the closed-loop BMI peripherals and synchronise the data flow. Peripherals include neural recorders & stimulators, data analysis cluster, video cameras and experimental chamber. Experimental chamber (variety of servos, steppers, sensors etc.) will be controlled with built-in FPGA and GPIO of SpeedGoat machine. Other peripherals are connected with serial bus. Acquired data needs to be organized and stored in datasink unit.
Skills:
Matlab Simulink, state-machines, FPGA programming, serial communication protocols, data synchronisation
Please send your CV and transcript along with your application. - Arithmetic and Logic Structures, Computer Communications Networks, Digital Systems, Electrical Engineering, Input, Output and Data Devices, Logic Design
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| Programming a graphical user interface (e.g. in Qt/C++) which can handle and process the data acquired in our brain-machine interface (BMI) experiments. The data includes high-density brain activity recordings from hundreds of recording channels, neural-stimulation events, 3D&4D data coming from MRI scans of the subject implanted with BMI. The backend will be programmed in Python where you also need to connect supporting tools (e.g. Blender) via Python.
Please send an email with your CV and transcript of records attached. - Electrical and Electronic Engineering, Software Engineering
- Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
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