Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Chicca, Elisabetta"'
Autor:
Cotteret, Madison, Greatorex, Hugh, Renner, Alpha, Chen, Junren, Neftci, Emre, Wu, Huaqiang, Indiveri, Giacomo, Ziegler, Martin, Chicca, Elisabetta
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attra
Externí odkaz:
http://arxiv.org/abs/2405.01305
Autor:
Suresh, Bharathwaj, Bertele, Martin, Breyer, Evelyn T., Klein, Philipp, Mulaosmanovic, Halid, Mikolajick, Thomas, Slesazeck, Stefan, Chicca, Elisabetta
Inspired by neurobiological systems, Spiking Neural Networks (SNNs) are gaining an increasing interest in the field of bio-inspired machine learning. Neurons, as central processing and short-term memory units of biological neural systems, are thus at
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A77196
https://tud.qucosa.de/api/qucosa%3A77196/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A77196/attachment/ATT-0/
In this paper, we present a novel spiking neural network model designed to perform frequency decomposition of spike trains. Our model emulates neural microcircuits theorized in the somatosensory cortex, rendering it a biologically plausible candidate
Externí odkaz:
http://arxiv.org/abs/2403.09723
Autor:
Mulaosmanovic, Halid, Chicca, Elisabetta, Bertele, Martin, Mikolajick, Thomas, Slesazeck, Stefan
Neuron is the basic computing unit in brain-inspired neural networks. Although a multitude of excellent artificial neurons realized with conventional transistors have been proposed, they might not be energy and area efficient in large-scale networks.
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A81343
https://tud.qucosa.de/api/qucosa%3A81343/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A81343/attachment/ATT-0/
Autor:
Nilsson, Mattias, Pina, Ton Juny, Khacef, Lyes, Liwicki, Foteini, Chicca, Elisabetta, Sandin, Fredrik
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of neuromorphic
Externí odkaz:
http://arxiv.org/abs/2301.09962
Autor:
Quintana, Fernando M., Perez-Peña, Fernando, Galindo, Pedro L., Neftci, Emre O., Chicca, Elisabetta, Khacef, Lyes
Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded systems. The next promise of brain-inspired computing is to enable adapt
Externí odkaz:
http://arxiv.org/abs/2301.08281
Hopfield attractor networks are robust distributed models of human memory, but lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may impleme
Externí odkaz:
http://arxiv.org/abs/2212.01196
Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with d
Externí odkaz:
http://arxiv.org/abs/2211.07761
Autor:
Khacef, Lyes, Klein, Philipp, Cartiglia, Matteo, Rubino, Arianna, Indiveri, Giacomo, Chicca, Elisabetta
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learn
Externí odkaz:
http://arxiv.org/abs/2209.15536
Publikováno v:
Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, vol 13024. Springer, Cham., pp. 297-312
Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames and yield sparse, energy-efficient encodings of scenes, in addition to low latency, high dynamic range, and lack of motion blur. Recen
Externí odkaz:
http://arxiv.org/abs/2112.03423