Double inverse stochastic resonance with dynamic synapses

Area of Science:

• Computational Neuroscience
• Neuronal Dynamics
• Synaptic Plasticity

Background:

• Neurons receive input from many presynaptic cells, creating complex synaptic currents.
• Neuronal response to noise can exhibit phenomena like stochastic resonance.
• Short-term synaptic plasticity (STP) dynamically alters synaptic strength.

Purpose of the Study:

• To investigate the impact of biophysically realistic noisy postsynaptic currents on model neuron behavior.
• To analyze the emergence of inverse stochastic resonance (ISR) under static and dynamic synaptic conditions.
• To explore how short-term synaptic plasticity (STP) modulates ISR and potentially leads to novel resonance phenomena.

Main Methods:

• Simulating a model neuron receiving postsynaptic currents driven by uncorrelated spiking activity.
• Analyzing neuronal firing rate response as a function of presynaptic firing rate.
• Implementing and comparing static synapses with dynamic synapses exhibiting short-term depression and facilitation.

Main Results:

• Static synapses can exhibit inverse stochastic resonance (ISR) with increasing presynaptic firing rate.
• Dynamic synapses with short-term plasticity can extend or diminish the firing rate range for ISR.
• Double inverse stochastic resonance (DISR), featuring two resonance peaks, was observed under certain plasticity conditions.

Conclusions:

• Short-term synaptic plasticity significantly influences the occurrence and characteristics of inverse stochastic resonance in model neurons.
• The interplay between synaptic dynamics and input noise can lead to complex resonance behaviors like DISR.
• These findings contribute to understanding how neuronal networks process information under realistic synaptic conditions.