Neuronal avalanches imply maximum dynamic range in cortical networks at criticality

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Spontaneous neuronal activity is fundamental to cortical function, influencing network output.
• Neuronal avalanches, a specific activity pattern, exhibit properties of critical systems.
• Criticality is theoretically linked to optimal input processing, but experimental validation is lacking.

Purpose of the Study:

• To experimentally test if networks generating neuronal avalanches are optimized for input processing.
• To investigate the relationship between criticality and the dynamic range of cortical networks.
• To explore how the balance of excitation and inhibition influences stimulus processing at criticality.

Main Methods:

• Utilized cortex slice cultures on microelectrode arrays for in vitro experiments.
• Manipulated the excitation-inhibition ratio to alter network dynamics.
• Measured network responses to stimuli and analyzed statistical properties of spontaneous activity.

Main Results:

• Demonstrated that cortical networks exhibiting neuronal avalanches possess a maximized dynamic range.
• Derived a network tuning curve showing stimulus processing efficiency varies with distance from criticality.
• Results align with theoretical predictions from computational simulations.

Conclusions:

• Balanced excitation and inhibition are crucial for establishing criticality in cortical networks.
• Criticality maximizes the range of inputs a network can effectively process.
• Spontaneous activity and input processing are interconnected phenomena within critical systems.