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Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression
- Source :
- Neural networks : the official journal of the International Neural Network Society. 37
- Publication Year :
- 2012
-
Abstract
- Theoretical considerations have led to the concept that the cerebral cortex is operating in a balanced state in which synaptic excitation is approximately balanced by synaptic inhibition from the local cortical circuit. This paper is about the functional consequences of the balanced state in sensory cortex. One consequence is gain control: there is experimental evidence and theoretical support for the idea that local circuit inhibition acts as a local automatic gain control throughout the cortex. Second, inhibition increases cortical feature selectivity: many studies of different sensory cortical areas have reported that suppressive mechanisms contribute to feature selectivity. Synaptic inhibition from the local microcircuit should be untuned (or broadly tuned) for stimulus features because of the microarchitecture of the cortical microcircuit. Untuned inhibition probably is the source of Untuned Suppression that enhances feature selectivity. We studied inhibition's function in our experiments, guided by a neuronal network model, on orientation selectivity in the primary visual cortex, V1, of the Macaque monkey. Our results revealed that Untuned Suppression, generated by local circuit inhibition, is crucial for the generation of highly orientation-selective cells in V1 cortex.
- Subjects :
- Neurons
Chemistry
Cognitive Neuroscience
Models, Neurological
Neural Inhibition
Sensory system
Visual system
Stimulus (physiology)
Article
Visual cortex
medicine.anatomical_structure
Artificial Intelligence
Cerebral cortex
Sensory threshold
Orientation
Sensory Thresholds
medicine
Biological neural network
Animals
Humans
Macaca
Visual Pathways
Neuroscience
Visual Cortex
Subjects
Details
- ISSN :
- 18792782
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Neural networks : the official journal of the International Neural Network Society
- Accession number :
- edsair.doi.dedup.....cb72497079e463b466694197bc15f999