arXiv subjects

Mahmut Akilli

Publications and source records attributed to Mahmut Akilli.

3 recordsLinked to original sources

arXiv2025

Synchronization of Weak Signals in Dynamic Systems

The present study proposes a methodology that combines the 'Duffing oscillator system' and the 'Kuramoto oscillator network' to explore the synchronization of weak signals in dynamic systems. The first step of the procedure is to detect weak periodic or quasi-periodic signals in noisy data collected from the quantifiable processes of any dynamical system using the Duffing oscillator system. The second step is to investigate how the interaction of these weak signals can be synchronized using the Kuramoto oscillator network model. This methodology was applied to seismic signals. The present study has shown that this methodology has great potential for investigating the weak signal synchronisation present within dynamic systems, as evidenced by the analysis of seismic data.

physics.soc-ph↗

arXiv2025

EEG Brain mapping based on the Duffing oscillator

This study proposes a Duffing oscillator based measurement framework for detecting frequency components associated with postsynaptic potential related activity in EEG recordings. The Duffing oscillator is employed as a nonlinear measurement system whose high sensitivity to weak periodic inputs enables robust frequency estimation in the presence of strong noise. Because postsynaptic potentials are embedded in noisy EEG recordings, they are treated as weak signals. Based on the detected weak-signal frequency distributions across EEG channels, topographic maps of brain activity are constructed to visualize spatial variations in neural activation. For this purpose, EEG signals recorded from two musicians and two audience members during a music experiment were analysed. The frequency components of the weak signals were searched within the 4 37 Hz range across all EEG channels. Subsequently, topographic brain maps were generated according to the number of detected weak-signal components. The results indicate that Duffing oscillator based weak signal detection is an effective tool for EEG brain mapping. Compared with Fourier and wavelet-based methods, the proposed technique provides a more detailed representation of brain activation. These findings suggest that the proposed approach has potential for investigating various pathological conditions and for enhancing the understanding of cognitive and behavioural functions of the human brain.

physics.soc-ph↗

arXiv2025

Measuring the entropy of a neuron cell from its membrane current signal

The purpose of this study was to investigate how the entropy of a neuron cell can be measured using membrane ion current signals, which were recorded from neurons in the mouse medial prefrontal cortex (mPFC). The sample entropy and the Scalogram entropy were used as entropy measurement methods. It is well known that the entropy increases in the direction of the movement of the system towards the equilibrium. Therefore, in the process of the electrical activity of a living cell, the entropy is expected to reach a maximum at the moment when the membrane potential reaches the 'Nernst equilibrium potential' (or ionic equilibrium) of the ions. However, it was observed that the entropy values obtained by traditional calculations did not reach the peak at the equilibrium state of the ions. Therefore, two modifications to these measurement methods were proposed to adjust the entropy value to the maximum at the equilibrium potential of the ions. As a result of these proposed modifications, the entropy values were observed to peak around the equilibrium potential of the ions. These refined approaches were successfully validated using the Logistic map. Additionally, the entropy results were compared with Lyapunov exponents. The results show that the behaviour of living cells can be analysed using entropy measurements. The results also suggest that the method could be used to detect differences in the behaviour of tumour and normal cells, or the effects of drugs on cells.

q-bio.NC↗