Contribution of synaptic depression to phase maintenance in a model rhythmic network

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Neuronal Oscillations

Background:

• Rhythmic neuronal networks often exhibit invariant firing phase despite frequency changes.
• Phase invariance suggests precise adjustments via intrinsic or synaptic mechanisms.
• Understanding phase maintenance is key to comprehending neuronal network function.

Purpose of the Study:

• To investigate phase maintenance in a computational model of an oscillator-follower neuron network.
• To compare phase-period relationships with depressing versus nondepressing inhibitory synapses.
• To analyze phase maintenance under conditions of constant active duration, inactive duration, or duty cycle.

Main Methods:

• Developed a computational model of an oscillator neuron inhibiting a follower neuron.
• Simulated network activity with both depressing and nondepressing inhibitory synapses.
• Varied the cycle period of the oscillator neuron by altering active duration, inactive duration, or duty cycle.
• Measured the firing phase of the follower neuron relative to the oscillator neuron's firing onset.

Main Results:

• Nondepressing synapses generally show monotonic phase-period relationships, with exceptions.
• Depressing synapses exhibit a cubic phase-period relationship (decrease-increase-decrease) in specific parameter regimes.
• The cubic relationship arises from the interplay between synaptic depression and postsynaptic neuron properties.
• Depressing synapses demonstrate superior, though not perfect, phase maintenance compared to nondepressing synapses.

Conclusions:

• Synaptic depression plays a significant role in stabilizing neuronal firing phase across varying cycle periods.
• The complex phase-period dynamics with depressing synapses allow for robust phase maintenance over wide frequency ranges.
• Depressing synapses offer a promising mechanism for enhancing the stability and reliability of rhythmic neuronal networks.