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Double inverse stochastic resonance with dynamic synapses.

Muhammet Uzuntarla1, Joaquin J Torres2, Paul So3

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Summary
This summary is machine-generated.

This study explores how synaptic plasticity affects neuronal noise response. Dynamic synapses can alter the conditions for inverse stochastic resonance (ISR) in model neurons, sometimes creating double ISR.

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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.