Added seminar Leyla Roksan Caglar

Author: brovelli

content/event/2025-06-26_seminaire-Leyla-Roksan-Caglar/index.md

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+---
+authors:
+- Andrea Brovelli
+date: 2025-06-26
+publishDate: 2025-05-12
+draft: false
+image:
+  focal_point: Center
+  placement: 2
+  preview_only: true
+projects: []
+tags:
+- events
+title: '2025-06-26 : CONECT seminar by Leyla Roksan Caglar'
+subtitle: '"Geometric Investigations of Representations, Learning, and Generalization in Minds, Brains, and Machines"'
+summary: 'CONECT seminar by Leyla Roksan Caglar: "Geometric Investigations of Representations, Learning, and Generalization in Minds, Brains, and Machines".'
+---
+
+
+* When: June 26th ***15:00 to 16:00*** 
+* Where: Salle Laurent Vinay, _Institut de Neurosciences de la Timone_, Marseille, France.
+
+During this CONECT seminar, [Leyla Roksan Caglar](https://www.leylaroksancaglar.com/) will present her work.
+
+> Understanding how abstract information is represented in the brain - and how such representations
+support efficient storage, retrieval, and generalization - has been a central challenge in cognitive
+science, neuroscience, and artificial intelligence. In a series of studies, I will illustrate how my
+research addresses these questions by 1) applying geometric and topological approaches to probe
+the structure of neural representations, and 2) by using comparative approaches across minds,
+brains, and machines. First, I will use a predictive encoding model and geometric analyses to show
+that object-directed action representations (e.g. of tools) contain motor information in a
+compositional manner that facilitates flexible retrieval. Then, I will use mathematical models of
+similarity to investigate the structure, shape, and metricity of representational manifolds and their
+congruency across behavioral and neural data. The results illustrate the importance between
+representational form and function, as well as the learning process’ representational constraints.
+Building on this, I will discuss some ongoing work exploring the intimate link between
+information-compression, generalization processes, and the shape of neural representations,
+illustrating how representations are dynamically adapted to task performance. Using a comparative
+lens across biological and artificial neural information processing systems and bridging
+information theoretical approaches with topological data analysis, this work aspires to uncover
+fundamental principles of generalization shared across humans, animals, and artificial neural
+networks.
+
+
+{{% callout note %}}
+TBA 
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