6 citations · 12 across the 11 of their papers we have counts for
11 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…
DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
Jann Krausse, Zhe Su, Kyrus Mama +4
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offerin…
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…
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…
Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm
Daniel Casanueva-Morato, Chenxi Wu, Giacomo Indiveri +2
Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propos…
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…
Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator
Alejandro Linares-Barranco, Luciano Prono, Robert Lengenstein +2
With the rise of artificial intelligence, neural network simulations of biological neuron models are being explored to reduce the footprint of learning and inference in resource-co…