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Taha Arda Kirdemir

Neuroscience & Neurotechnology

Taha Arda Kirdemir

Independent Researcher

Exploring mathematical frameworks and computational approaches for neural representation learning, cross-subject alignment, and Brain-Computer Interfaces.

Explore ResearchView Publications

01

About

Taha Arda Kirdemir is an independent researcher focusing on neuroscience, neurotechnology, and Brain-Computer Interfaces. His research interests include computational approaches to neural representation learning, mathematical modeling, and cross-subject neural alignment.

Name
Taha Arda Kirdemir
Role
Independent Researcher
Research fields
  • Neuroscience
  • Neurotechnology
  • Brain-Computer Interfaces (BCI)
  • Computational Neuroscience
  • Neural Representation Learning
  • Mathematical Modeling

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Research Interests

Areas of ongoing interest. These descriptions outline research directions and theoretical frameworks; they do not imply experimental validation.

  1. 01

    Brain-Computer Interfaces

    Systems that translate recorded neural activity into signals for communication and control, with attention to how decoding generalizes beyond a single recording session or user.

  2. 02

    Computational Neuroscience

    Using computational models and quantitative analysis to describe how neural populations encode, transform, and represent information.

  3. 03

    Neural Representation Learning

    Learning compact latent representations of high-dimensional neural recordings that preserve task-relevant structure while discarding nuisance variability.

  4. 04

    Cross-Subject Neural Alignment

    Relating neural activity recorded from different individuals within a shared representation, so that models do not need to be rebuilt from scratch for every subject.

  5. 05

    Graph-Based Representation Matching

    Describing neural data as graphs and seeking soft, probabilistic correspondences between their nodes across subjects, instead of enforcing rigid one-to-one assignments.

  6. 06

    Mathematical Modeling

    Formulating neural alignment and latent-space projection as precise mathematical problems, with explicit assumptions that can be analyzed and tested.

03

Publications

Preprint

Zenodo

Cross-Subject Neural Alignment via Soft Graph Matching and Dynamic Latent Space Projection for Brain-Computer Interfaces

Author: Taha Arda Kirdemir

A mathematical framework for cross-subject neural alignment using soft graph matching, dynamic temporal adjustment, and universal latent space projection.

DOI: https://doi.org/10.5281/zenodo.23842524

Read Publication: Cross-Subject Neural Alignment via Soft Graph Matching and Dynamic Latent Space Projection for Brain-Computer Interfaces (opens Zenodo in a new tab)

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Academic Profiles

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Get in Touch

For research-related inquiries and academic correspondence, feel free to get in touch.

kirdemirtahaarda@gmail.com