Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected...

Area of Science:

• Computational Neuroscience
• Neural Network Dynamics
• Brain Rhythmic Activity

Background:

• Inhibitory interneurons in the hippocampus and cortex are crucial for generating rhythmic brain activity.
• Network connectivity and intrinsic cellular properties significantly influence neuronal firing synchronization and clustering.

Purpose of the Study:

• To investigate how intrinsic cellular properties (IF curve, PRC) and network connectivity affect clustered dynamics in inhibitory neural networks.
• To analyze the bursting properties of networks composed of Type I and Type II neurons, with and without M-type adaptation currents.

Main Methods:

• Simulated randomly connected, heterogeneous inhibitory neural networks.
• Quantified intrinsic cellular properties using current-frequency (IF) curves and Phase Response Curves (PRCs).
• Analyzed network dynamics under varying cellular heterogeneity, firing frequency, and synaptic inhibition decay time scales.

Main Results:

• Type I neuron networks synchronized into a single active cluster.
• Type II neuron networks segregated into two mutually exclusive clusters based on intrinsic firing frequencies.
• The presence of an M-type adaptation current in Type II neurons modulated cluster dynamics differently compared to Type I or standard Type II networks.

Conclusions:

• Intrinsic neuronal properties and network connectivity are key determinants of clustered dynamics in inhibitory networks.
• Divergent network dynamics were observed compared to predictions from the interneuron network gamma (ING) mechanism and all-to-all connected networks.
• Neuronal PRCs, when perturbed by synaptic current profiles, revealed mechanisms underlying the distinct network dynamics observed across neuron types.