1 code implementation • 19 Jan 2023 • Fernando M. Quintana, Fernando Perez-Peña, Pedro L. Galindo, Emre O. Neftci, Elisabetta Chicca, Lyes Khacef
We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure.
1 code implementation • 21 Nov 2020 • Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi, Ahmed M. Eltawil, Emre O. Neftci
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity.
no code implementations • 22 Oct 2020 • Friedemann Zenke, Emre O. Neftci
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams.
no code implementations • 5 Mar 2020 • Clemens JS Schaefer, Patrick Faley, Emre O. Neftci, Siddharth Joshi
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters.
4 code implementations • 28 Jan 2019 • Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing.
no code implementations • 14 Nov 2015 • Emre O. Neftci, Bruno U. Pedroni, Siddharth Joshi, Maruan Al-Shedivat, Gert Cauwenberghs
Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex.
