| An event-driven classifier for spiking neural networks fed with synthetic or dynamic vision sensor data E Stromatias, M Soto, T Serrano-Gotarredona, B Linares-Barranco Frontiers in neuroscience 11, 350, 2017 | 159 | 2017 |
| Robustness of spiking deep belief networks to noise and reduced bit precision of neuro-inspired hardware platforms E Stromatias, D Neil, M Pfeiffer, F Galluppi, SB Furber, SC Liu Frontiers in neuroscience 9, 222, 2015 | 144 | 2015 |
| Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on spinnaker E Stromatias, D Neil, F Galluppi, M Pfeiffer, SC Liu, S Furber 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 108 | 2015 |
| Power analysis of large-scale, real-time neural networks on SpiNNaker E Stromatias, F Galluppi, C Patterson, S Furber The 2013 international joint conference on neural networks (IJCNN), 1-8, 2013 | 106 | 2013 |
| On practical issues for stochastic STDP hardware with 1-bit synaptic weights A Yousefzadeh, E Stromatias, M Soto, T Serrano-Gotarredona, ... Frontiers in neuroscience 12, 665, 2018 | 76 | 2018 |
| A framework for plasticity implementation on the SpiNNaker neural architecture F Galluppi, X Lagorce, E Stromatias, M Pfeiffer, LA Plana, SB Furber, ... Frontiers in neuroscience 8, 429, 2015 | 65 | 2015 |
| Benchmarking spike-based visual recognition: a dataset and evaluation Q Liu, G Pineda-Garcia, E Stromatias, T Serrano-Gotarredona, SB Furber Frontiers in neuroscience 10, 496, 2016 | 41 | 2016 |
| Breaking the millisecond barrier on SpiNNaker: implementing asynchronous event-based plastic models with microsecond resolution X Lagorce, E Stromatias, F Galluppi, LA Plana, SC Liu, SB Furber, ... Frontiers in neuroscience 9, 206, 2015 | 33 | 2015 |
| Hybrid neural network, an efficient low-power digital hardware implementation of event-based artificial neural network A Yousefzadeh, G Orchard, E Stromatias, T Serrano-Gotarredona, ... 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2018 | 18 | 2018 |
| Supervised learning in spiking neural networks with limited precision: Snn/lp E Stromatias, JS Marsland 2015 International Joint Conference on Neural Networks (IJCNN), 1-7, 2015 | 18 | 2015 |
| The 2013 International Joint Conference on Neural Networks (IJCNN) E Stromatias, D Neil, F Galluppi, M Pfeiffer, SC Liu, S Furber IJCNN, 2013 | 14 | 2013 |
| Developing a supervised training algorithm for limited precision feed-forward spiking neural networks E Stromatias arXiv preprint arXiv:1109.2788, 2011 | 13 | 2011 |
| Live demonstration: handwritten digit recognition using spiking deep belief networks on SpiNNaker E Stromatias, D Neil, F Galluppi, M Pfeiffer, SC Liu, S Furber 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 1901-1901, 2015 | 12 | 2015 |
| Scalability and robustness of artificial neural networks E Stromatias PQDT-UK & Ireland, 2016 | 6 | 2016 |
| Neuron circuit, system, and method with synapse weight learning B Linares-barranco, A Yousefzadeh, E Stromatias, T Serrano-gotarredona US Patent 11,301,753, 2022 | 4 | 2022 |
| An event-based classifier for dynamic vision sensor and synthetic data E Stromatias, M Soto, MT Serrano Gotarredona, B Linares Barranco Frontiers Media, 2017 | 4 | 2017 |
| Optimising the overall power usage on the SpiNNaker neuromimetic platform E Stromatias, C Patterson, S Furber 2014 International Joint Conference on Neural Networks (IJCNN), 4280-4287, 2014 | 3 | 2014 |
| State-of-the-art deep learning has a carbon emission problem. Can neuromorphic engineering help? E Stromatias Dialogues in Clinical Neuroscience & Mental Health 3 (3), 143-149, 2020 | 1 | 2020 |
| 6. From Activations to Spikes F Galluppi, TS Gotarredona, Q Liu, E Stromatias | | |
Steve FurberProfessor Emeritus, Department of Computer Science, The University of Manchester, UKPatvirtintas el. paštas manchester.ac.uk
