Difference between revisions of "Pathways to the 2023 IHP thematic program Random Processes in the Brain/Seminars"
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* Date: Tuesday, November 8, 2022 | * Date: Tuesday, November 8, 2022 | ||
* Abstract: Shannon's information theory has served as a bedrock for advances in communication and storage systems over the past five decades. However, this theory does not handle well higher order structures (e.g., graphs, geometric structures), temporal aspects (e.g., real-time considerations), or semantics. We argue that these are essential aspects of data and information that underly a broad class of current and emerging data science applications. In this talk, we present some recent results on structural and temporal information. We first show how to extract temporal information in dynamic networks (arrival of nodes) from its structure (unlabeled graphs). We then proceed to establish fundamental limits on information content for some data structures, and present asymptotically optimal lossless compression algorithms achieving these limits for various graph models. | * Abstract: Shannon's information theory has served as a bedrock for advances in communication and storage systems over the past five decades. However, this theory does not handle well higher order structures (e.g., graphs, geometric structures), temporal aspects (e.g., real-time considerations), or semantics. We argue that these are essential aspects of data and information that underly a broad class of current and emerging data science applications. In this talk, we present some recent results on structural and temporal information. We first show how to extract temporal information in dynamic networks (arrival of nodes) from its structure (unlabeled graphs). We then proceed to establish fundamental limits on information content for some data structures, and present asymptotically optimal lossless compression algorithms achieving these limits for various graph models. | ||
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+ | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section_forthcoming|'''Forthcoming:''' Olivier Faugeras|2}} | ||
+ | ;Mathematical Neuroscience | ||
+ | * Speaker: Olivier Faugeras, Inria Sophia Antipolis | ||
+ | * Date: Tuesday, October 25, 2022 | ||
+ | * Abstract: Why is it important to ground neuroscience in mathematics ? What kind of mathematics are relevant in this scientific area where biology, perception; action and cognition are closely intermingled ? What kind of relationships should be entertained with experimentalists and computationalists ? In this lecture I will try to answer these questions through examples drawn from the analysis of the activity of large populations of neurons by mathematical methods from probability, statistics, and geometry. | ||
{{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section_forthcoming|'''Forthcoming:''' Tilo Schwalger|2}} | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section_forthcoming|'''Forthcoming:''' Tilo Schwalger|2}} | ||
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* Date: Tuesday, October 11, 2022 | * Date: Tuesday, October 11, 2022 | ||
* Abstract: TBA | * Abstract: TBA | ||
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{{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Markus Diesmann|2}} | {{Pathways to the 2023 IHP thematic program Random Processes in the Brain/Section|Markus Diesmann|2}} |
Revision as of 13:36, 5 September 2022
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