Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Local, recurrent connections are a hallmark of cortical circuits.
• Their precise impact on neuronal responses remains debated, with conflicting reports of excitation, suppression, or weak effects.

Purpose of the Study:

• To investigate the functional impact of local recurrent connections in mouse V1 excitatory neurons.
• To reconcile diverse experimental observations regarding recurrent circuit dynamics.

Main Methods:

• Optogenetic stimulation of excitatory neurons in mouse primary visual cortex (V1).
• Analysis of neuronal response patterns, including excitation and suppression.
• Development and application of a balanced-state network model.

Main Results:

• Optogenetic input induced heterogeneous "salt-and-pepper" patterns of neuronal excitation and suppression.
• Individual neuron responses were poorly predicted by direct input alone.
• A balanced-state network model successfully explained the observed dynamics, including suppressed responses and decoupling of input-output.
• The model revealed strong and variable excitatory-excitatory recurrent connections.

Conclusions:

• Dense excitatory recurrent connections significantly shape neuronal responses in V1.
• These connections are crucial for cortical computations, dynamically altering neuron output in response to input.
• Network models are valuable for understanding complex recurrent circuit functions.