744 citations · 5.9k across the 174 of their papers we have counts for
177 papers
Efficiency measurements of GEM GE1/1 chambers in the upgraded CMS Endcap Muon System using 2023 collision data at TeV
M. Abbas, S. Abbott, M. Abbrescia +184
The CMS experiment at the Large Hadron Collider employs Gas Electron Multiplier (GEM) detectors, a technology based on gaseous ionization, as one of the muon detectors. The muon sp…
Online Data Reduction with Spiking Neural Networks: A Temporal-Coincidence Encoder and Distributed SNN for the ePIC dRICH Detector
Pierpaolo Perticaroli, Roberto Ammendola, Andrea Biagioni +10
The dual-radiator Ring Imaging Cherenkov (dRICH) detector of the ePIC experiment at the Electron-Ion Collider (EIC) will read out 320,000 silicon photomultiplier (SiPM) chann…
AIGOR: A Modular, Event-Driven Neuromorphic Architecture for Configurable SNN Inference
Pierpaolo Perticaroli, Roberto Ammendola, Andrea Biagioni +10
Spiking neural networks (SNNs) run today on a fragmented landscape of hardware: dedicated neuromorphic processors, application-specific FPGA accelerators, and large-scale neuroscie…
APEIRON: composing smart TDAQ systems for high energy physics experiments
Roberto Ammendola, Andrea Biagioni, Carlotta Chiarini +12
We present APEIRON, a distributed heterogeneous processing framework comprising both hardware architecture and software stack for multi-FPGA systems. Targeting smart trigger and da…
Cherenkov and scintillation light separation in BGO and BSO crystals coupled to SiPMs for dual-readout electromagnetic calorimetry at future colliders
M. Alviggi, B. Argiento, E. Auffray +29
We report on the separation of Cherenkov and scintillation light in BGO and BSO crystals read out with silicon photomultipliers (SiPMs). The two light components are disentangled o…
NET4EXA: Pioneering the Future of Interconnects for Supercomputing and AI
Michele Martinelli, Roberto Ammendola, Andrea Biagioni +41
NET4EXA aims to develop a next-generation high-performance interconnect for HPC and AI systems, addressing the increasing demands of large-scale infrastructures, such as those requ…
Unsupervised sleep-like intra- and inter-layer plasticity categorizes and improves energy efficiency in a multilayer spiking network
Leonardo Tonielli, Cosimo Lupo, Elena Pastorelli +4
Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain ci…
Multiscale dynamical characterization of cortical brain states: from synchrony to asynchrony
Maria V. Sanchez-Vives, Arnau Manasanch, Andrea Pigorini +17
The cerebral cortex spontaneously displays different patterns of activity that evolve over time according to the brain state. Sleep, wakefulness, resting states, and attention are…
State-dependent brain responsiveness, from local circuits to the whole brain
A. Destexhe, J Goldman, N. Tort-Colet +32
The objective of this paper is to review physiological and computational aspects of the responsiveness of the cerebral cortex to stimulation, and how responsiveness depends on the…
Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Gianluca Gaglioti, Alessandra Cardinale, Cosimo Lupo +10
The simulation of whole-brain dynamics should reproduce realistic spontaneous and evoked neural activity across different scales, including emergent rhythms, spatio-temporal activa…
CMS RPC Non-Physics Event Data Automation Ideology
A. Dimitrov, M. Tytgat, K. Mota Amarilo +101
This paper presents a streamlined framework for real-time processing and analysis of condition data from the CMS experiment Resistive Plate Chambers (RPC). Leveraging data streamin…
Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej +5
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual…
Production and validation of industrially produced large-sized GEM foils for the Phase-2 upgrade of the CMS muon spectrometer
Inseok Yoon
The upgrade of the CMS detector for the high luminosity LHC (HL-LHC) will include gas electron multiplier (GEM) detectors in the end-cap muon spectrometer. Due to the limited suppl…
APEIRON: composing smart TDAQ systems for high energy physics experiments
Roberto Ammendola, Andrea Biagioni, Carlotta Chiarini +11
APEIRON is a framework encompassing the general architecture of a distributed heterogeneous processing platform and the corresponding software stack, from the low level device driv…
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Bruno Golosio, Jose Villamar, Gianmarco Tiddia +6
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also ho…
Characterization of a gas detector prototype based on Thick-GEM for the MAGNEX focal plane detector
I. Ciraldo, G. A. Brischetto, D. Torresi +18
A new gas detector prototype for the upgrade of the focal plane detector of the MAGNEX large-acceptance magnetic spectrometer has been developed and tested in view of the NUMEN pro…
Machine Learning based tool for CMS RPC currents quality monitoring
E. Shumka, A. Samalan, M. Tytgat +105
The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile…
RPC based tracking system at CERN GIF++ facility
K. Mota Amarilo, A. Samalan, M. Tytgat +105
With the HL-LHC upgrade of the LHC machine, an increase of the instantaneous luminosity by a factor of five is expected and the current detection systems need to be validated for s…
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most…
Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
Robin Gutzen, Giulia De Bonis, Chiara De Luca +11
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, a…
NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model
Chiara De Luca, Leonardo Tonielli, Elena Pastorelli +8
Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in po…
Towards biologically plausible Dreaming and Planning in recurrent spiking networks
Cristiano Capone, Pier Stanislao Paolucci
Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performance…
Quality Control of Mass-Produced GEM Detectors for the CMS GE1/1 Muon Upgrade
M. Abbas, M. Abbrescia, H. Abdalla +179
The series of upgrades to the Large Hadron Collider, culminating in the High Luminosity Large Hadron Collider, will enable a significant expansion of the physics program of the CMS…
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons an…