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

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• Cortical neural networks exhibit asynchronous states punctuated by coordinated activity.
• Existing models like inhibitory stabilized networks (ISN) and depression-stabilized excitatory networks (DSEN) fail to capture the full spectrum of cortical activity patterns.

Purpose of the Study:

• To develop a unified model that explains the transition between asynchronous and synchronous states in cortical networks.
• To elucidate the roles of synaptic depression and balanced excitation-inhibition in generating population events.

Main Methods:

• Analysis of spontaneously active in vitro mouse auditory cortex slices.
• Development of firing rate and biophysically realistic spiking network models.
• Incorporation of balanced excitation-inhibition with short-term synaptic depression on excitatory synapses.

Main Results:

• The model successfully reproduces both asynchronous states and transient population events observed experimentally.
• Synaptic depression onto inhibitory neurons initiates population events.
• Synaptic depression onto excitatory neurons shapes the rhythmicity (2-12 Hz oscillations) within these events.
• The model unifies balanced and depression-stabilized network theories.

Conclusions:

• A novel model integrating balanced networks and synaptic depression explains cortical population dynamics.
• This framework provides a mechanistic understanding of nonlinear, population-wide correlations in the cortex.
• The findings offer insights into neural synchrony and information processing in the brain.