Local cortical circuit model inferred from power-law distributed neuronal avalanches

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Cortical neuron circuit organization dictates information processing.
• Spontaneous synchronous neuronal activity exhibits repeatable patterns known as "neuronal avalanches."
• Neuronal avalanches follow power-law distributions, hinting at underlying network structures, but these remain unclear.

Purpose of the Study:

• To investigate the explicit network structure responsible for power-law statistics in neuronal avalanches.
• To develop a computational model that replicates stable avalanche-like activity in cortical circuits.
• To elucidate the wiring rules governing neuronal network development and their impact on activity patterns.

Main Methods:

• Development of a computational neuronal network model incorporating pyramidal and inhibitory neurons.
• Simulation of network development using a novel wiring rule promoting mutually overlapping cell assemblies.
• Analysis of network topology, including feedforward chains and recurrent circuits, and their relationship to avalanche stability.
• Investigation of how cell-assembly formation and inhibitory feedback influence power-law statistics.

Main Results:

• The model successfully generated stable avalanche-like spiking activity.
• A specific wiring rule was identified that leads to the formation of overlapping cell assemblies.
• Networks dominated by feedforward chains, rather than recurrent circuits, exhibited stable neuronal avalanches.
• Recurrent connections constrained the density of cell assemblies within a given neuron pool.

Conclusions:

• The study proposes a wiring rule that explains the formation of complex cortical circuits underlying neuronal avalanches.
• Network topology, particularly the balance between feedforward and recurrent connections, is critical for stable avalanche propagation.
• Cortical local circuits may possess a more intricate topological organization than previously assumed.