Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders

This study aims to use machine learning to find miniature excitatory postsynaptic currents (EPSCs) in neurons of a Drosophila to find behavior markers of a seizure. Using MATLAB, we are training a machine learning model on electrophysiological data to recognize patterns of post-synaptic events that...

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Main Authors: Cengiz Gunay, Krishan Bhalsod
Format: Article
Language:English
Published: LibraryPress@UF 2025-05-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
Online Access:https://journals.flvc.org/FLAIRS/article/view/138939
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author Cengiz Gunay
Krishan Bhalsod
author_facet Cengiz Gunay
Krishan Bhalsod
author_sort Cengiz Gunay
collection DOAJ
description This study aims to use machine learning to find miniature excitatory postsynaptic currents (EPSCs) in neurons of a Drosophila to find behavior markers of a seizure. Using MATLAB, we are training a machine learning model on electrophysiological data to recognize patterns of post-synaptic events that show potential seizure activity. We have faced challenges applying this method and we are planning to present these in our poster. The results of this research may help develop a further understanding of seizure mechanisms in Drosophila that could translate into a more in-depth understanding for neurological disorders in humans.
format Article
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issn 2334-0754
2334-0762
language English
publishDate 2025-05-01
publisher LibraryPress@UF
record_format Article
series Proceedings of the International Florida Artificial Intelligence Research Society Conference
spelling doaj-art-2836218f14fe47409f404c4bb2d24d4e2025-08-20T02:30:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138939Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure DisordersCengiz Gunay0Krishan Bhalsod1https://orcid.org/0009-0007-1338-054XGeorgia Gwinnett CollegeGeorgia Gwinnett College This study aims to use machine learning to find miniature excitatory postsynaptic currents (EPSCs) in neurons of a Drosophila to find behavior markers of a seizure. Using MATLAB, we are training a machine learning model on electrophysiological data to recognize patterns of post-synaptic events that show potential seizure activity. We have faced challenges applying this method and we are planning to present these in our poster. The results of this research may help develop a further understanding of seizure mechanisms in Drosophila that could translate into a more in-depth understanding for neurological disorders in humans. https://journals.flvc.org/FLAIRS/article/view/138939time series analysisevent detectionMODoptimal filterneuronsynapse
spellingShingle Cengiz Gunay
Krishan Bhalsod
Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
Proceedings of the International Florida Artificial Intelligence Research Society Conference
time series analysis
event detection
MOD
optimal filter
neuron
synapse
title Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
title_full Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
title_fullStr Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
title_full_unstemmed Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
title_short Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders
title_sort applying a machine learning method to detect changing neuronal activity in seizure disorders
topic time series analysis
event detection
MOD
optimal filter
neuron
synapse
url https://journals.flvc.org/FLAIRS/article/view/138939
work_keys_str_mv AT cengizgunay applyingamachinelearningmethodtodetectchangingneuronalactivityinseizuredisorders
AT krishanbhalsod applyingamachinelearningmethodtodetectchangingneuronalactivityinseizuredisorders