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The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex
Daniel B Rubin1, Stephen D Van Hooser2, Kenneth D Miller3
1Center for Theoretical Neuroscience, Doctoral Program in Neurobiology and Behavior, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
Neuron
|January 23, 2015
Summary
Neurons in sensory cortex integrate information through surround suppression and normalization. A new theory explains these complex integration properties using four fundamental cortical circuit properties, confirmed in visual cortex recordings.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Neuronal integration in the sensory cortex is crucial for perception.
- Key properties include surround suppression and normalization, which vary with input strength.
Purpose of the Study:
- To present a general theory explaining neuronal integration properties.
- To identify fundamental cortical circuit properties underlying integration.
Main Methods:
- Development of a theoretical model of cortical circuits.
- Experimental recordings in visual cortex to test model predictions.
Main Results:
- The theory robustly explains surround suppression and normalization.
- Four circuit properties (supralinear I/O, recurrent excitation, feedback inhibition, spatial connections) generate integrative properties.
- Excitatory and inhibitory neurons exhibit similar integrative behaviors.
Conclusions:
- A simple, general theory explains complex neuronal integration.
- Cortical circuit properties dynamically generate observed integrative phenomena.
- Model predictions are validated by new experimental data.
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