Phase transitions towards criticality in a neural system with adaptive interactions

Area of Science:

• Computational Neuroscience
• Theoretical Physics
• Complex Systems

Background:

• Self-organized criticality (SOC) is a key concept for understanding brain function.
• Neural networks exhibit complex dynamics, including phase transitions.
• Dynamical synapses with depression and facilitation are crucial for neural computation.

Purpose of the Study:

• To analytically describe a new transition scenario to self-organized criticality (SOC).
• To investigate the interplay between different types of phase transitions and SOC in neural networks.
• To explore the role of synaptic dynamics and noisy inputs in controlling brain states.

Main Methods:

• Analytical description of a neural network model.
• Analysis of pulse-coupled neurons with dynamical synapses (depression and facilitation).
• Investigation of phase transitions using bifurcation theory.

Main Results:

• A novel transition scenario to SOC is analytically described.
• The model exhibits coexistence of a SOC phase and a subcritical phase.
• A cusp bifurcation connects the SOC and subcritical phases.
• Switching between phases is controllable by varying noisy input intensity.

Conclusions:

• The findings offer a new framework for understanding critical brain dynamics.
• This work bridges concepts from statistical physics and neuroscience.
• The results highlight the importance of synaptic plasticity and noise in neural computation.