no code implementations • 10 May 2023 • Dániel L Barabási, Ginestra Bianconi, Ed Bullmore, Mark Burgess, SueYeon Chung, Tina Eliassi-Rad, Dileep George, István A. Kovács, Hernán Makse, Christos Papadimitriou, Thomas E. Nichols, Olaf Sporns, Kim Stachenfeld, Zoltán Toroczkai, Emma K. Towlson, Anthony M Zador, Hongkui Zeng, Albert-László Barabási, Amy Bernard, György Buzsáki
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
1 code implementation • 11 Nov 2021 • Rajiv Sambasivan, Mark Burgess, Jörg Schad, Arthur Keen, Christopher Woodward, Alexander Geenen, Sachin Sharma
This graph is refined by applying sparsity-based statistical learning methods.
no code implementations • 23 Sep 2020 • Mark Burgess
The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case.
no code implementations • 23 Sep 2020 • Mark Burgess
Given a pool of observations selected from a sensor stream, input data can be robustly represented, via a multiscale process, in terms of invariant concepts, and themes.
no code implementations • 12 Jul 2019 • Mark Burgess
To understand and explain process behaviour we need to be able to see it, and decide its significance, i. e. be able to tell a story about its behaviours.
no code implementations • 12 Feb 2017 • Mark Burgess
This is an expensive and static approach which depends heavily on the availability of a very particular kind of prior raining data to make inferences in a single step.
no code implementations • 7 Aug 2016 • Mark Burgess
Using the previously developed concepts of semantic spacetime, I explore the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory.
