Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks
Date
2019-01-01
Author
Akan, Adil Kaan
Kivilcim, Baran Baris
Akbaş, Emre
Newman, Sharlene D.
Yarman Vural, Fatoş Tunay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
197
views
0
downloads
Cite This
It is hypothesized that the process of complex problem solving in human brain consists of two basic phases, namely, planning and execution. In this study, we propose a computational model in order to verify this hypothesis. For this purpose, we develop a holistic approach for decoding the planning and execution phases of complex problem solving, using the functional magnetic resonance imaging data (fMRI), recorded when the subjects play the Tower of London (TOL) game. In the first step of the proposed study, we estimate a brain network, called Artificial Brain Network (ABN), by designing an artificial neural network, whose weights correspond to the edge weights of the brain network established among the anatomic regions. Then, we decode the planning and execution tasks of complex problem slowing by training a multi-layer perceptron. It is shown that the edge weights of the artificial brain network capture the functional connectivity among anatomic brain regions. When trained on the edge weights of brain networks extracted from average BOLD activation of anatomical regions, the proposed model successfully discriminates the planning and execution phases of complex problem solving process. We compare the suggested computational brain network model to the state of the art models reported in the literature and observe that the decoding performance of the suggested model is better then the available methods in the literature.
Subject Keywords
fMRI
,
Brain decoding
,
Neural networks
,
Brain networks
URI
https://hdl.handle.net/11511/42253
DOI
https://doi.org/10.1109/siu.2019.8806456
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Analyzing Complex Problem Solving by Dynamic Brain Networks
Alchihabi, Abdullah; Ekmekci, Ömer; Kivilcim, Baran B.; Newman, Sharlene D.; Yarman Vural, Fatos T. (2021-12-01)
Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested...
Modeling Voxel Connectivity for Brain Decoding
Onal, Itir; Ozay, Mete; Yarman Vural, Fatoş Tunay (2015-06-12)
The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study, we propose a local mesh model, called Local Mesh Model with Temporal Measurements (LMM-TM), to first estim...
Improvement of temporal resolution of fMRI data for brain decoding
Varol, Emel; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2022-2-10)
In this study, we aim to increase the accuracy of the mapping between the states of the brain and problem-solving phases namely planning and execution. To create a computational model to generate the mapping, an fMRI dataset obtained from subjects solving the Tower of London problem has been used. fMRI data is suitable for this problem as it provides regional and time-varying changes in brain metabolism. However, developing the model using fMRI data is not trivial. Generally, fMRI data has a very large feat...
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
Rahnama, Arash; Alchihabi, Abdullah; Gupta, Vijay; Antsaklis, Panos J.; Yarman Vural, Fatoş Tunay (2017-10-25)
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the...
Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. K. Akan, B. B. Kivilcim, E. Akbaş, S. D. Newman, and F. T. Yarman Vural, “Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42253.