Interleaving asynchronous and synchronous activity in balanced cortical networks with short-term synaptic depression

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Neural Dynamics

Background:

• Cortical populations exhibit asynchronous states interrupted by coordinated activity, a phenomenon not fully explained by current models.
• Existing network models either stabilize asynchronous states but fail to produce population-wide activity or generate transient activity but neglect inhibition's role.

Purpose of the Study:

• To develop computational models that can replicate the mixed state dynamics observed in cortical activity.
• To investigate the mechanisms underlying the transition between asynchronous and coordinated population activity in the cortex.

Main Methods:

• Analysis of spontaneously active in vitro primary auditory cortex preparations.
• Development of firing rate-based and biologically realistic spiking neuron network models.
• Incorporation of balanced excitation-inhibition with short-term synaptic depression in excitatory connections.

Main Results:

• The developed models successfully mimic the mixture of asynchronous and coordinated population activity seen in experimental data.
• Models exhibit nonlinear behaviors, including low-frequency (2-12 Hz) rhythmic dynamics during population events.
• Synaptic depression was identified as a key mechanism that weakens inhibitory recruitment, thereby triggering population events.

Conclusions:

• The study successfully extends balanced network models to incorporate short-term synaptic depression, explaining complex cortical dynamics.
• This approach provides a mechanistic framework for understanding realistic cortical activity patterns, bridging the gap between asynchronous and synchronous states.
• The findings offer a significant step towards a comprehensive theory of neural population dynamics in the cortex.