Computational and experimental modulation of a noisy chaotic neuronal system

Area of Science:

• Computational Neuroscience
• Neurophysiology
• Nonlinear Dynamics

Background:

• Neuronal state transitions are crucial for brain function.
• Period doubling cascades are a known route to chaos in dynamical systems.
• The role of noise in these transitions is not fully understood.

Purpose of the Study:

• To investigate the interplay between chaos and noise in neuronal state transitions.
• To model period doubling cascades in neuronal activity.
• To compare computational findings with experimental data from a biological neuron.

Main Methods:

• Implemented a mathematical model of a neuron subjected to neuromodulatory input and noise.
• Performed equivalent experimental studies on a biological neuron from the crab Cancer borealis stomatogastric ganglion.
• Analyzed neuronal state transitions, focusing on tonic and bursting regimes.

Main Results:

• Simulations demonstrated transitions between tonic and bursting regimes via chaos and period doubling cascades.
• The presence of intrinsic noise in the model made the transition less evident.
• Noisy computational outputs showed features similar to experimental results.

Conclusions:

• Chaos and period doubling cascades play a significant role in neuronal state transitions.
• Intrinsic noise can alter the manifestation of these transitions.
• Computational models, even with noise, can capture key features of biological neuronal dynamics.