T. McGinnity

Cognitive Neuroscience · with Belatreche · 2005–2019

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 39 citations into it
• Electrical and Electronic Engineering — 34 citations into it
• Artificial Intelligence — 12 citations into it

Whom T. McGinnity trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Eugene M. Izhikevich
2. Sophie Denève
3. Wulfram Gerstner

Papers from T. McGinnity's perspective

Ordered by how strongly the researchers T. McGinnity trusts endorse each paper. Same corpus, a different order for every researcher.

1. SPIKING NEURON MODELS Single Neurons , Populations , Plasticity
2. Long short-term memory and Learning-to-learn in networks of spiking neurons (Neural Information Processing Systems)
3. Error-backpropagation in temporally encoded networks of spiking neurons
4. Simple model of spiking neurons
5. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing (IEEE International Joint Conference on Neural Network)
6. The spike timing dependence of plasticity
7. Which model to use for cortical spiking neurons? (IEEE Transactions on Neural Networks)
8. Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks (IEEE Signal Processing Magazine)
9. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (Neural Computation)
10. Deep Learning With Spiking Neurons: Opportunities and Challenges (Frontiers in Neuroscience)

Their papers

• A review of learning in biologically plausible spiking neural networks (2019) — 384 citations
• A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware (2005) — 70 citations