The human brain project.

By Egidio D'Angelo. 2012

289 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “…contest won by DL (Ciresan, Giusti, Gambardella, & Schmidhuber, 2012), again through an GPU-MPCNN ensemble (Segmentation of Neuronal Structures in EM Stacks Challenge, 2012).2 EM stacks are relevant for the recently approved huge brain projects in Europe and the US (e.g., Markram, 2012).”
JUST: Large-Scale Multi-Tier Storage Infrastructure at the Jülich Supercomputing Centre (2019, methodology): “One JUST subsystem was funded by the "Interactive Computing E-Infrastructure for the Human Brain Project" project (Human Brain Project, 2021).”
Neural networks and neuroscience-inspired computer vision. (2014, background): “Several groups have launched large, multinational efforts to simulate parts of or whole brains in silico [15].”
Could a Neuroscientist Understand a Microprocessor? (2016, background): “Scientists are beginning to reconstruct connectivity [3], record activity [4], and simulate computation [5] at unprecedented scales.”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “Progress on theoretical and computational neuroscience is accelerating dramatically, also thanks to largescale funding initiatives recently announced in both Europe and the United States [172], [173].”
Emerging Memory Devices for Neuromorphic Computing (2019, result): “[82] A recent study to estimate the level of parallelism in the human brain using fMRI data, revealed interesting results.”
Controlling Recurrent Neural Networks by Conceptors (2014, result): “Quite to the contrary, two of the largest current research initiatives worldwide, the Human Brain Project [18] and the NIH BRAIN initiative [51], are ultimately driven by this problem.”
Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations (2014, background): “projected to require an exascale supercomputer (10(18) flops) [4] and as much power as a quarter-million households (0.”
A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, background): “part of the European Union’s Human Brain Project [20],”
IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis (2020, background): “Furthermore, a similar paradigm is used to reverse-engineer the brain to advance knowledge in various brain-related disciplines (Markram, 2012) or seeking interpretation for reinforcement learning agents (Verma et al., 2018).”