Principles of visual cortex excitatory microcircuit organization

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding microcircuit function requires detailed knowledge of synapse-specific connectivity and dynamics.
• Classic paired recordings offer low throughput, limiting comprehensive analysis of neural circuits.
• The primary visual cortex (V1) serves as a model system for studying cortical computation.

Purpose of the Study:

• To develop and implement a high-throughput method for mapping synaptic connectivity.
• To investigate the principles of excitatory input organization onto different interneuron types in V1.
• To uncover layer-specific connectivity patterns and synaptic properties within the V1 microcircuit.

Main Methods:

• Optomapping: A high-throughput two-photon optogenetic method enabling rapid mapping of synaptic inputs.
• Electrophysiological recordings were performed in mouse V1 to characterize synaptic responses.
• Analysis of synaptic efficacy distribution, input layer specificity, and short-term plasticity.

Main Results:

• Optomapping successfully identified 1,790 excitatory inputs to pyramidal, basket, and Martinotti cells in V1.
• Log-normal distribution of synaptic efficacies was a common principle across cell types.
• Unexpected layer-specific excitation patterns were observed: basket cells primarily in layer 5, Martinotti cells in layer 2/3.
• Basket cells received stronger and more widespread excitation than pyramidal cells, potentially enhancing circuit stability.
• Short-term plasticity demonstrated dependence on both cortical layer and target cell type.
• Interconnected layer-6 pyramidal cells showed an overrepresentation of shared inputs.

Conclusions:

• Optomapping significantly enhances the throughput for mapping synaptic connectivity, overcoming limitations of traditional methods.
• The study reveals novel principles of V1 microcircuit organization, including layer-specific input targeting of interneurons.
• Findings highlight the importance of synaptic efficacy distributions, layer-dependent plasticity, and shared input organization in shaping V1 function.