Math Marius Yamakou

Browse all posts in the Math Marius Yamakou category.

3 total

01OCT 2020
Math
Neural networks and Machine Learning
Neural networks and Machine Learning Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural Networks with time delayed connections Neurons communicate with each other through electrical signals. It is well known that these signals are oscillatory and that the properties of the oscillations depend on the characteristics of the individual neurons, how the neurons are connected, and […]Neural networks and Machine Learning Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural Networks with time delayed connections Neurons communicate with each other through electrical signals. It is well known that these signals are oscillatory and that the properties of the oscillations depend on the characteristics of the individual neurons, how the neurons are connected, and […]
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23SEP 2020
Math
Stochastic Synchronization of Chaotic Neurons
Stochastic Synchronization of Chaotic Neurons Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Real biological neurons can show chaotic dynamics when excited by the certain external input current. The behavior of these neurons is characterized by instability and, as a result, limited predictability in time. Mathematically, a system is chaotic if it has a long-term aperiodic […]Stochastic Synchronization of Chaotic Neurons Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Real biological neurons can show chaotic dynamics when excited by the certain external input current. The behavior of these neurons is characterized by instability and, as a result, limited predictability in time. Mathematically, a system is chaotic if it has a long-term aperiodic […]
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31AUG 2020
Math
Stochastic Neural Dynamics
Stochastic Neural Dynamics Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural activity shows fluctuations and unpredictable transitions in its dynamics. This randomness can be an integral aspect of neuronal function; examples range from discrete fluctuations of ion channels to sudden sleep stage transitions involving the entire brain. To understand brain function as well as dysfunctions (i.e., […]Stochastic Neural Dynamics Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural activity shows fluctuations and unpredictable transitions in its dynamics. This randomness can be an integral aspect of neuronal function; examples range from discrete fluctuations of ion channels to sudden sleep stage transitions involving the entire brain. To understand brain function as well as dysfunctions (i.e., […]
Read more about Stochastic Neural Dynamics