Neuromorphic Cognitive Systems Group - Indiveri, GiacomoOpen OpportunitiesThis 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
| 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
| 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
| Water management is critical to agriculture, especially with increasing climate change and water scarcity concerns. Traditional water supply systems often rely on fixed schedules or basic sensor feedback to control irrigation or water distribution, which can result in inefficiencies and wastage. With the rapid advancements in machine learning and neuromorphic computing, there is an opportunity to develop smarter, more adaptive water supply systems. Spiking Neural Networks (SNNs) on a hardware substrate[1], inspired by the brain's efficient way of processing information, offer a promising solution due to their event-based processing and low energy consumption.
This project proposes the development of a neuromorphic implementation of a Spiking Neural Network on DYNP-SE1[2] to optimize water supply using real-time moisture datasets [3]. The SNN will learn and adapt to environmental changes, ensuring that water is supplied only when necessary, reducing waste, and optimizing water usage. - Agricultural Engineering
- Bachelor Thesis, Master Thesis, Semester Project
| 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
| This project explores the relationship between corticomuscular coherence (CMC) and muscular and
cognitive fatigue during dual-task performance. We will utilize synchronized EMG and EEG data collected with the
The Quattrocento system from OTBioelettronica will investigate variations in CMC between
the onset of EMG and EEG signals.
- Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Internship, Master Thesis, Semester Project
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