898 citations · 965 across the 11 of their papers we have counts for
11 papers
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks
Christoph Weilenmann, Alexandros Ziogas, Till Zellweger +6
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex fun…
Self-Supervised Learning Through Efference Copies
Franz Scherr, Qinghai Guo, Timoleon Moraitis
Self-supervised learning (SSL) methods aim to exploit the abundance of unlabelled data for machine learning (ML), however the underlying principles are often method-specific. An SS…
Hebbian Deep Learning Without Feedback
Adrien Journé, Hector Garcia Rodriguez, Qinghai Guo +1
Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. M…
Short-Term Plasticity Neurons Learning to Learn and Forget
Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis
Short-term plasticity (STP) is a mechanism that stores decaying memories in synapses of the cerebral cortex. In computing practice, STP has been used, but mostly in the niche of sp…
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
Alan Jeffares, Qinghai Guo, Pontus Stenetorp +1
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. in the rank order in which neurons fire, whereas artificial neural networks (ANNs…
Chalcogenide optomemristors for multi-factor neuromorphic computation
Syed Ghazi Sarwat, Timoleon Moraitis, C David Wright +1
Neural processing on devices and circuits is fast becoming a popular approach to emulate biological neural networks. Elaborate CMOS and memristive technologies have been employed t…
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks
Timoleon Moraitis, Dmitry Toichkin, Adrien Journé +2
Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for neuromorphic on-chip learning, owing to its efficient, local, unsupervised, and on-line nature. Moreov…
Optimality of short-term synaptic plasticity in modelling certain dynamic environments
Timoleon Moraitis, Abu Sebastian, Evangelos Eleftheriou
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication a…
File Classification Based on Spiking Neural Networks
Ana Stanojevic, Giovanni Cherubini, Timoleon Moraitis +1
In this paper, we propose a system for file classification in large data sets based on spiking neural networks (SNNs). File information contained in key-value metadata pairs is map…
Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, S. R. Nandakumar +7
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which…
Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes
Timoleon Moraitis, Abu Sebastian, Irem Boybat +3
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of e…