Six types of multistability in a neuronal model based on slow calcium current

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Mathematical Biology

Background:

• Multistability, the coexistence of multiple dynamic states, is common in excitable systems like neurons.
• While functional roles in memory and posture are suggested, mechanisms for multistability in bursting neuronal regimes remain unclear.
• Multistability may offer evolutionary advantages for neurons in complex networks like Central Pattern Generators.

Purpose of the Study:

• To investigate the biophysical mechanisms behind coexisting oscillatory and silent regimes in a neuronal model.
• To classify the different types of multistability observed in neuronal dynamics.
• To understand the parameter dependencies of multistable states.

Main Methods:

• Development of a low-dimensional model representing a leech heart interneuron.
• Bifurcation analysis to explore the model's dynamical regimes.
• Identification of separating regimes (saddle periodic orbits or equilibria) governing multistability.

Main Results:

• The model exhibits six distinct types of multistability.
• These include coexistence of bursting and silence, tonic spiking and silence, tonic spiking and subthreshold oscillations, bursting and subthreshold oscillations, and bursting, subthreshold oscillations, and silence.
• The parameter ranges for multistability are determined by the emergence and termination of separating regimes.

Conclusions:

• A novel neuronal model demonstrating diverse multistability was developed.
• A new mechanism for the bistability of bursting and silence was identified.
• The model serves as a valuable tool for studying neuronal network dynamics with varied multistability.