11 citations · 12 across the 8 of their papers we have counts for
9 papers
Precise Positional Readout of Molecular Barcode Structures using Solid-State Nanopores
Wouter Botermans, Eric Beamish, Matteo Cartiglia +7
Fast and nonuniform translocation through solid-state nanopores limits both the detection of small molecular labels and their precise localization along molecular carriers. In this…
Latent space mapping of interpretable structural coordinates from stochastic single-molecule signals
Matteo Cartiglia, Sandro Kuppel, Wouter Botermans Wannes Peeters +7
Nanopores are versatile single-molecular sensors, but their utility is fundamentally constrained by stochastic translocation dynamics warping any encoded information. We resolve it…
Data Sieving for Scalable Real-Time Multichannel Nanopore Sensing
Matteo Cartiglia, Natan Biesmans, Wannes Peeters +9
High-throughput solid-state nanopore experiments generate continuous MHz-rate data streams in which only a small fraction of data contains informative molecular information. This c…
SPAIC: A sub-W/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders
Shyam Narayanan, Matteo Cartiglia, Arianna Rubino +3
Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several ne…
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
Arianna Rubino, Matteo Cartiglia, Melika Payvand +1
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural netw…
Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits
Lyes Khacef, Philipp Klein, Matteo Cartiglia +3
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptiv…
Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems
Matteo Cartiglia, Arianna Rubino, Shyam Narayanan +4
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip on…
Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
Nik Dennler, Germain Haessig, Matteo Cartiglia +1
Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems.…
An error-propagation spiking neural network compatible with neuromorphic processors
Matteo Cartiglia, Germain Haessig, Giacomo Indiveri
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algori…