Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Neurobiology

Background:

• Synaptic plasticity, particularly spike-timing-dependent plasticity (STDP), is crucial for learning and memory.
• Understanding how STDP shapes neuronal circuit connectivity is an ongoing challenge.
• Prior research primarily focused on firing rate's role in connectivity patterns.

Purpose of the Study:

• To investigate how STDP organizes neuronal connectivity beyond firing rate descriptions.
• To develop a theory incorporating both firing rate and firing variability.
• To identify distinct network structure regimes governed by STDP.

Main Methods:

• Developed a self-consistent linear response theory.
• Incorporated firing rate and firing variability into the model.
• Decomposed pairwise spike correlations into direct and indirect connection components.

Main Results:

• Identified two distinct regimes of network structure formation under STDP.
• One regime aligns with firing rate-based connectivity descriptions.
• A second, distinct regime emerges when indirect connection correlations dominate, driven by heterogeneous firing variability.

Conclusions:

• Neuronal network structure is shaped by STDP through both direct and indirect connection dynamics.
• Firing variability heterogeneity induces temporal asymmetry in indirect correlations, leading to novel network structures.
• High-order statistics of neural activity play a significant role in spike-correlation-sensitive learning.