Neuron populations across layer 2-6 in the mouse visual cortex exhibit different coding abilities in the awake mice

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• The visual cortex, crucial for visual processing, has six layers with distinct neuronal properties.
• Decoding properties vary across these layers, influencing visual information processing.

Purpose of the Study:

• To investigate the relationship between visual stimulus decoding properties and specific cortical layers in the mouse visual cortex.
• To examine how this relationship differs across various decoders and brain regions (VISp, VISm, VISl).

Main Methods:

• Analysis of two-photon microscopy data from mouse visual cortex neuronal responses.
• Application and comparison of various decoders (including Support Vector Machines - SVM) to assess decoding accuracy.

Main Results:

• Decoding accuracy varied significantly across visual cortical layers for stimuli like gratings and natural images.
• Layer 4 in VISp showed superior decoding accuracy for visual stimulus identity compared to other layers.
• Layer 2/3 in VISm outperformed layer 4, though overall accuracy was lower than VISp and VISl.
• SVM decoder achieved the highest accuracy, with consistent layer-wise performance variations across decoders.
• Decoding accuracy correlated positively with neuronal population reliability and signal-to-noise ratio (SNR), but not orientation or direction selectivity indices.

Conclusions:

• Cortical layer and decoder choice significantly impact visual decoding accuracy.
• Reliability and SNR are key factors influencing decoding performance across visual cortex layers.
• Findings provide nuanced insights into visual cortex functioning and information processing across layers.