Philip Tully
Title
Cited by
Cited by
Year
Weaponizing Data Science for Social Engineering: Automated E2E Spearphishing on Twitter
J Seymour, PJ Tully
Black Hat USA 2016, DEF CON 24, 2016
562016
Synaptic and nonsynaptic plasticity approximating probabilistic inference
PJ Tully, MH Hennig, A Lansner
Frontiers in synaptic neuroscience 6, 8, 2014
422014
Spike-based Bayesian-Hebbian learning of temporal sequences
PJ Tully, H Lindén, MH Hennig, A Lansner
PLoS computational biology 12 (5), e1004954, 2016
322016
Large-scale simulations of plastic neural networks on neuromorphic hardware
JC Knight, PJ Tully, BA Kaplan, A Lansner, SB Furber
Frontiers in neuroanatomy 10, 37, 2016
192016
Functional relevance of different basal ganglia pathways investigated in a spiking model with reward dependent plasticity
P Berthet, M Lindahl, PJ Tully, J Hellgren-Kotaleski, A Lansner
Frontiers in neural circuits 10, 53, 2016
162016
Generative models for spear phishing posts on social media
J Seymour, P Tully
arXiv preprint arXiv:1802.05196, 2018
92018
Repurposing Neural Networks to Generate Synthetic Media for Information Operations
P Tully, L Foster
Black Hat USA 2020, 2020
22020
Probabilistic computation underlying sequence learning in a spiking attractor memory network
P Tully, H Lindén, MH Hennig, A Lansner
BMC Neuroscience 14 (1), 1-2, 2013
22013
Methods for automated social phishing
J Foster, M Price, CB Cullison, P Tully, JJ Seymour III
US Patent App. 15/944,254, 2019
12019
Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
P Tully
KTH Royal Institute of Technology, 2017
12017
A Picture is Worth a Thousand Words, Literally: Deep Neural Networks for Social Stego
P Tully, MT Raggo
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Articles 1–11