Institute of Neuroinformatics

Open Opportunities

Master Thesis: Fraternal Inhibition in Songbird Brother ETH Zurich Institute of Neuroinformatics Our 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

Time-invariant shape recognition using tactile sensors on neuromorphic hardware (in collaboration with the University of Chicago). University of Zurich Neuromorphic Cognitive Systems Group - Indiveri, Giacomo 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

Title: Neuromorphic Sensor Integration for Plant Health Monitoring University of Zurich Neuromorphic Cognitive Systems Group - Indiveri, Giacomo This project will be carried out in collaboration with the FHNW Institute for Sensors and Electronics Monitoring plant health is crucial for early detection of pests, identifying anomalies, and ensuring timely interventions. While numerous sensors are available for this purpose, selecting the most effective ones and eliminating redundancy remains a challenge. Additionally, transmitting large volumes of data to the cloud is power-intensive, especially in resource-constrained environments. To address these challenges, local preprocessing is essential to reduce data load and enhance efficiency. Leveraging neuromorphic hardware provides a promising approach to achieve low-power, real-time processing for plant status monitoring. Engineering and Technology Bachelor Thesis, Master Thesis, Semester Project

Exploiting Heterogeneity in Neuromorphic Systems for Robust Analog Computing University of Zurich Neuromorphic Cognitive Systems Group - Indiveri, Giacomo Neuromorphic computing represents a cutting-edge approach to designing computational systems by mimicking the architecture and functionality of biological neurons. One of the persistent challenges in fabricating neuromorphic devices is the cross-device response variability, which is often seen as a limitation. However, biological neurons and synapses are intrinsically heterogeneous, exhibiting a wide spectrum of responses that enhance robustness and adaptability. Inspired by this, recent computational study[1] demonstrated that neural networks composed of heterogeneous neurons—without the need for plasticity—significantly outperform their homogeneous counterparts, particularly in their reliability across a range of temporal tasks. [1] Golmohammadi et al 2024, https://arxiv.org/abs/2412.05126 [2] Zendrikov et al, 2023 10.1088/2634-4386/ace64c Engineering and Technology Master Thesis, Semester Project

Objects classification on Tactile Information with Neuromorphic Hardware University of Zurich Neuromorphic Cognitive Systems Group - Indiveri, Giacomo This project aims to develop a neuromorphic system for object classification using tactile data, inspired by the human sense of touch. By integrating biomimetic sensors and a neuromorphic chip, the system processes spatiotemporal tactile information with high efficiency and low power consumption. The approach leverages spiking neural networks (SNNs) to encode and shapes in real time. The project focuses on designing algorithms optimized for the unique properties of neuromorphic hardware and evaluating performance in dynamic, real-world scenarios. This work has potential applications in robotics, prosthetics, and intelligent sensing systems, offering an energy-efficient solution for tactile perception tasks. Engineering and Technology, Medical and Health Sciences Bachelor Thesis, Master Thesis, Semester Project

Developing a real-time state-machine for closed-loop brain-machine interfaces ETH Zurich Neurotechnology 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

Developing a GUI for cross-modality brain-machine interfaces ETH Zurich Neurotechnology 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