{"id":4272,"date":"2026-01-16T11:24:03","date_gmt":"2026-01-16T11:24:03","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4272"},"modified":"2026-01-16T11:26:26","modified_gmt":"2026-01-16T11:26:26","slug":"damsl-288-analysis-and-modelling-of-brain-networks","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-288-analysis-and-modelling-of-brain-networks\/","title":{"rendered":"DAMSL-288 Analysis and Modelling of Brain Networks"},"content":{"rendered":"\t\t
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Type<\/strong><\/p>

Elective<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Course Code<\/strong><\/p>

DAMSL-288<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Teaching Semester<\/strong><\/p>

B semester<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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ECTS Credits<\/strong><\/p>

10<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Student Performance Evaluation<\/h6>

Homework and\/or Lab Assignments, Final Exam and\/or Project<\/p><\/div><\/div><\/div>

Prerequisite Courses<\/h6>

Data Structures, Probabilities, Applied Mathematics<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Syllabus<\/span><\/h6>

The course includes the following parts:<\/p>

  1. Fundamental Concepts in Neuroscience, neurophysiology; neurons; neuronal anatomy; synaptic transmission; the role of neurotransmitters; basics in cognitive neuroscience<\/li>
  2. Neuronal models (e.g., leaky integrate and fire model); Neural Computations, Communications and Ensembles<\/li>
  3. Concepts and techniques from graph theory and network science (small worldness, network robustness)<\/li>
  4. Applying techniques from statistical analysis and machine learning (e.g., statistical tests, clustering algorithms, classification, correlations, dimensionality reduction)<\/li>
  5. Imaging tools, experimental methods, monitoring, and data collection (e.g., two-photon imaging, optogenetics MRI, patch clamping)<\/li>
  6. Advanced topics in network neuroscience, including neuroscience-driven AI, e.g., continual learning, neuromorphic computing<\/li><\/ol>

    During the class, the students work in data analysis projects, focusing on the visual cortex and how it encodes the various stimuli, propagates the activity between layers and areas, and represents the behavior. Furthermore, it analyzes functional connectivity under various conditions and in the context of neurological diseases.<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Learning Outcomes<\/span><\/h6>
    • Knowledge:<\/strong>\u00a0Having attended and succeeded in the course, the student is able to understand aspects related to the neuronal activity under different conditions, neuronal models, and the characterization of the neuronal network architecture.<\/li>
    • Understanding:<\/strong>\u00a0Having attended and succeeded in the course, the student will have an understanding of the main principles of neuronal functional connectivity, physiology, and network neuroscience.<\/li>
    • Application:<\/strong>\u00a0Having attended and succeeded in the course, the student is able to apply algorithms and techniques from graph theory, network science, and machine learning to characterize the behavior of the functional networks and neuronal activity.<\/li>
    • Analysis:<\/strong>\u00a0Having attended and succeeded in the course, the student is able to analyze biological data (e.g., two-photon imaging data), to characterize the connectivity and the neuronal activity of various populations under different conditions.<\/li>
    • Synthesis:<\/strong>\u00a0Having attended and succeeded in the course, the student is able to combine ideas and techniques from graph theory, network science, and machine learning, as well as data from different imaging tools, in order to characterize the neuronal activity and connectivity under different conditions (e.g., stimulus presentation, in the context of neurological diseases);<\/li>
    • Evaluation:<\/strong>\u00a0Having attended and succeeded in the course, the student is able to assess the changes of the functional connectivity and activity of various neuronal populations, in the presence of various conditions.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      <\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Type Elective Course Code DAMSL-288 Teaching Semester B semester ECTS Credits 10 Student Performance Evaluation Homework and\/or Lab Assignments, Final Exam and\/or Project Prerequisite Courses Data Structures, Probabilities, Applied Mathematics Syllabus The course includes the following parts: Fundamental Concepts in Neuroscience, neurophysiology; neurons; neuronal anatomy; synaptic transmission; the role of neurotransmitters; basics in cognitive neuroscience Neuronal models (e.g., leaky integrate and fire model); Neural Computations, Communications and Ensembles Concepts and techniques from graph theory and network science (small worldness, network robustness) Applying techniques from statistical analysis and machine learning (e.g., statistical tests, clustering algorithms, classification, correlations, dimensionality reduction) Imaging tools, experimental methods, monitoring, and data collection (e.g., two-photon imaging, optogenetics MRI, patch clamping) Advanced topics in network neuroscience, including neuroscience-driven AI, e.g., continual learning, neuromorphic computing During the class, the students work in data analysis projects, focusing on the visual cortex and how it encodes the various stimuli, propagates the activity between layers and areas, and represents the behavior. Furthermore, it analyzes functional connectivity under various conditions and in the context of neurological diseases. Learning Outcomes Knowledge:\u00a0Having attended and succeeded in the course, the student is able to understand aspects related to the neuronal activity under different conditions, neuronal models, and the characterization of the neuronal network architecture. Understanding:\u00a0Having attended and succeeded in the course, the student will have an understanding of the main principles of neuronal functional connectivity, physiology, and network neuroscience. Application:\u00a0Having attended and succeeded in the course, the student is able to apply algorithms and techniques from graph theory, network science, and machine learning to characterize the behavior of the functional networks and neuronal activity. Analysis:\u00a0Having attended and succeeded in the course, the student is able to analyze biological data (e.g., two-photon imaging data), to characterize the connectivity and the neuronal activity of various populations under different conditions. Synthesis:\u00a0Having attended and succeeded in the course, the student is able to combine ideas and techniques from graph theory, network science, and machine learning, as well as data from different imaging tools, in order to characterize the neuronal activity and connectivity under different conditions (e.g., stimulus presentation, in the context of neurological diseases); Evaluation:\u00a0Having attended and succeeded in the course, the student is able to assess the changes of the functional connectivity and activity of various neuronal populations, in the presence of various conditions.<\/p>\n","protected":false},"author":194,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-4272","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/comments?post=4272"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4272\/revisions"}],"predecessor-version":[{"id":4306,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4272\/revisions\/4306"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}