211 citations · 445 across the 18 of their papers we have counts for
19 papers
Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation
Federica Ferrari, Flavia Davidhi, Bernard Maacaron +6
Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mech…
Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
Jiahui An, Chonghao Cai, Olympia Gallou +3
This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking N…
Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Jiahui An, Sara Irina Fabrikant, Giacomo Indiveri +1
Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance a…
EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka +2
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR naviga…
Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays
Farah Baracat, Agnese Grison, Dario Farina +2
Restoring naturalistic finger control in assistive technologies requires the continuous decoding of motor intent with high accuracy, efficiency, and robustness. Here, we present a…
Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
Farah Baracat, Giacomo Indiveri, Elisa Donati
Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require lar…
Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition
Yuqi Ding, Elisa Donati, Haobo Li +1
Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission to centralized processing unit w…
Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements
Agnese Grison, Jaime Ibanez Pereda, Silvia Muceli +5
Decoding the activity of the nervous system is a critical challenge in neuroscience and neural interfacing. In this study, we present a neuromuscular recording system that enables…
Neuromorphic Heart Rate Monitors: Neural State Machines for Monotonic Change Detection
Alessio Carpegna, Chiara De Luca, Federico Emanuele Pozzi +4
Detecting monotonic changes in heart rate (HR) is crucial for early identification of cardiac conditions and health management. This is particularly important for dementia patients…
A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures
Fernando M. Quintana, Maryada, Pedro L. Galindo +3
Developing dedicated mixed-signal neuromorphic computing systems optimized for real-time sensory-processing in extreme edge-computing applications requires time-consuming design, f…
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Rachel Sava, Elisa Donati, Giacomo Indiveri
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processi…
Long-term stable Electromyography classification using Canonical Correlation Analysis
Elisa Donati, Simone Benatti, Enea Ceolini +1
Discrimination of hand gestures based on the decoding of surface electromyography (sEMG) signals is a well-establish approach for controlling prosthetic devices and for Human-Machi…
Neuromorphic implementation of ECG anomaly detection using delay chains
Stefan Gerber, Marc Steiner, Maryada +2
Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware. One promising approach is…
Organic log-domain integrator synapse
Mohammad Javad Mirshojaeian Hosseini, Elisa Donati, Giacomo Indiveri +1
Synapses play a critical role in memory, learning, and cognition. Their main functions include converting pre-synaptic voltage spikes to post-synaptic currents, as well as scaling…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Neuromorphic Pattern Generation Circuits for Bioelectronic Medicine
Elisa Donati, Renate Krause, Giacomo Indiveri
Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired lo…
Adaptive Extreme Edge Computing for Wearable Devices
Erika Covi, Elisa Donati, Hadi Heidari +4
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networ…
Closed-loop spiking control on a neuromorphic processor implemented on the iCub
Jingyue Zhao, Nicoletta Risi, Marco Monforte +3
Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the devel…
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian +4
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorit…