Hierarchical excitatory synaptic connectivity in mouse olfactory cortex.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Cortical columns are known topological motifs in brain information processing.
• The mesoscale topology of cortical networks beyond columns is largely unknown.
• The olfactory cortex lacks a clear columnar structure, and its network organization has been considered random.

Purpose of the Study:

• To investigate the excitatory network topology in the mouse olfactory cortex.
• To determine the organizational principles underlying network structure and activity patterns.
• To test whether random, columnar, distributed, or hierarchical network models best fit experimental data.

Main Methods:

• Variance analysis of paired whole-cell recordings in olfactory cortex slices.
• Analysis of network-wide bursts in disinhibited slices.
• Simulation of excitatory network models with varying topologies (random, local, distributed, power-law, hierarchical).

Main Results:

• Network-wide bursts exhibited similar time courses across widely separated pyramidal neurons, despite trial-to-trial variability.
• Random network models partially reproduced experimental burst patterns.
• Models with local (columnar) or distributed subnetworks showed greater variability than observed.
• Network models with power-law and hierarchical connectivity provided the best fit to the data.

Conclusions:

• Distributed subnetworks appear weak or absent in the olfactory cortex.
• A hierarchical excitatory topology likely predominates in the olfactory cortex.
• This hierarchical organization may underlie burst generation and shape sensory information processing.