Orthogonalization of spontaneous and stimulus-driven activity by hierarchical neocortical areal network in primates
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View abstract on PubMed
Summary
This summary is machine-generated.Neural networks separate sensory signals from noise using orthogonal activity patterns. This study reveals this signal-noise separation mechanism is a general principle across cortical areas and species.
Area Of Science
- Neuroscience
- Computational Neuroscience
Background
- Biological neural networks must reliably process information amidst spontaneous neural activity.
- In mouse primary visual cortex (V1), orthogonal patterns of spontaneous and evoked activity aid signal-noise separation.
- Studies in carnivore and primate V1 show similar patterns, suggesting different mechanisms in columnar cortices.
Purpose Of The Study
- To investigate the mechanism of signal-noise separation in the marmoset visual system.
- To compare spontaneous and stimulus-evoked activity patterns across different cortical areas in marmosets.
Main Methods
- Electrophysiological recordings in marmoset primary visual cortex (V1) and higher visual areas.
- Analysis of spontaneous and stimulus-evoked neural activity patterns.
- Comparison of activity pattern similarity and orthogonality.
Main Results
- In marmoset V1, spontaneous and stimulus-evoked activity patterns were similar.
- In higher marmoset visual areas, activity patterns became progressively orthogonalized along the cortical hierarchy.
- Orthogonalization levels in higher marmoset areas matched those observed in mouse V1.
Conclusions
- Orthogonalization of spontaneous and stimulus-evoked activity is a general principle of cortical computation.
- This mechanism for separating sensory signals from internal noise is conserved across different species and visual areas.
- The findings challenge previous assumptions about signal-noise separation in columnar visual cortices.
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