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Stabilized Supralinear Network Model of Responses to Surround Stimuli in Primary Visual Cortex.
Dina Obeid1,2, Kenneth D Miller1,3
1Center for Theoretical Neuroscience and Swartz Program in Theoretical Neuroscience, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027 dinaobeid@seas.harvard.edu ken@neurotheory.columbia.edu.
Eneuro
|April 14, 2025
Summary
We developed a circuit model explaining surround suppression in the visual cortex. This model accurately predicts how surrounding stimuli affect neural responses, particularly feature-specific suppression, offering insights into visual information integration.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Visual Processing
Background:
- Mammalian primary visual cortex (V1) exhibits complex interactions between stimuli in the classical receptive field (CRF) and its surround.
- Understanding the circuit mechanisms of these interactions is key to deciphering general cortical information integration strategies.
Purpose of the Study:
- To develop a circuit model that explains key features of surround suppression in V1.
- To investigate the circuit mechanisms underlying feature-specific suppression and orientation-tuned surround modulation.
Main Methods:
- Development of a stabilized supralinear network (SSN) model.
- Simulations incorporating biologically plausible connectivity, synaptic efficacies dependent on cortical distance and orientation difference.
- Modeling both rate-based and conductance-based spiking units.
Main Results:
- The SSN model successfully reproduced surround suppression, including feature-specific suppression and orientation-matching effects.
- The model demonstrated that feature-specific suppression and orientation tuning of suppression are independent phenomena.
- The model replicated the rapid activity decay observed in mouse V1 upon thalamic input silencing.
Conclusions:
- The developed SSN model provides a mechanistic explanation for complex surround modulation in V1.
- The findings suggest distinct circuit mechanisms underlie feature-specific suppression and orientation-tuned surround effects.
- The model's ability to reproduce experimental observations validates its utility in understanding cortical computation.
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