Inhibitory connectivity defines the realm of excitatory plasticity.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Recent experiments reveal substantial volatility in excitatory synaptic connections, even without learning.
• This finding challenges the established hypothesis that stable synapses are essential for long-term information storage in the brain.

Purpose of the Study:

• To measure ongoing synaptic volatility within cortical networks.
• To employ theoretical modeling to investigate the impact of synaptic volatility on cortical dynamics and information maintenance.

Main Methods:

• Experimental measurement of synaptic volatility in neural networks.
• Theoretical modeling of cortical dynamics under conditions of synaptic volatility.

Main Results:

• In a balanced cortical network, neural activity patterns are predominantly governed by inhibitory connectivity, despite the prevalence of excitatory synapses.
• The inhibitory network demonstrates superior efficacy in storing memory patterns compared to the excitatory network.
• Network activity remains robust against excitatory synaptic volatility, provided the balance between excitation and inhibition is preserved.

Conclusions:

• The study hypothesizes that inhibitory connectivity, rather than excitatory, plays a crucial role in the long-term maintenance and loss of information in volatile cortical networks.
• This suggests a paradigm shift in understanding how the brain stores information, emphasizing the dynamic role of inhibition.