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Published on: June 29, 2018
Temperature-modulated synchronization transition in coupled neuronal oscillators.
Yasuomi D Sato1, Keiji Okumura, Akihisa Ichiki
1Department of Brain Science and Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Wakamatsu, Kitakyushu, Japan. sato-y@brain.kyutech.ac.jp
Summary
We investigated how temperature scaling affects neuron firing and synchronization. Small temperature changes can alter firing frequency and synchronization patterns in neural networks, offering new insights into neural dynamics.
Area of Science:
- Computational neuroscience
- Biophysics
- Theoretical neuroscience
Background:
- Neuronal activity is characterized by frequency-current (f-I) curves and phase response curves (PRCs).
- The intrinsic temperature scaling parameter (μ) influences ionic channel dynamics, affecting neuronal firing properties.
- Understanding these properties is crucial for characterizing neural network behavior.
Purpose of the Study:
- To investigate the impact of the temperature scaling parameter (μ) on neuronal firing properties (f-I curves and PRCs).
- To analyze synchronization phenomena in a two-neuron network under varying μ.
- To explore temperature modulation as a mechanism for tuning neural synchrony.
Main Methods:
- Characterization of neuronal activity using frequency-current (f-I) curves and phase response curves (PRCs).
- Analysis of the effects of the intrinsic temperature scaling parameter (μ) on ionic channel dynamics.
- Application of the phase-reduction method to study synchronization in a two-neuron network.
Main Results:
- A peak in firing frequency was observed for small μ in class I neurons, differing from previous findings.
- The phase response curves (PRCs) exhibited a type II form on a logarithmic f-I curve when μ was small.
- Common μ-dependent transitions and bifurcations in synchronization were identified in the two-neuron network, independent of input current (I).
Conclusions:
- The study reveals novel μ-dependent firing properties in neurons, particularly a distinct peak frequency.
- Temperature modulation significantly influences neural synchronization, showing predictable transitions and bifurcations.
- These findings provide insights into how temperature variations can tune network synchronization, relevant for understanding neural oscillations and information processing.
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