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Neuronal heterogeneity of normalization strength in a circuit model
Deying Song1,2, Douglas Ruff3, Marlene Cohen3
1Joint Program in Neural Computation and Machine Learning, Neuroscience Institute, and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA.
Biorxiv : the Preprint Server for Biology
|November 28, 2024
Summary
Neurons in the visual cortex show varied normalization strengths, linked to inhibitory currents. This heterogeneity enhances information processing and efficiency in neural networks, offering computational benefits.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Visual Cortex Function
Background:
- Neuronal receptive field size increases along the visual hierarchy.
- Higher-order visual neurons use normalization for information integration.
- Normalization strength is heterogeneous across visual cortex neurons.
Purpose of the Study:
- To investigate the circuit mechanisms behind heterogeneous normalization in the visual cortex.
- To model normalization in a spiking neuron network of the visual cortex.
- To understand the computational benefits of neuronal heterogeneity in normalization.
Main Methods:
- Developed a spiking neuron network model of the visual cortex.
- Analyzed the relationship between normalization strength and inhibitory currents.
- Compared model-generated spike count correlations with experimental data.
Main Results:
- Model exhibited heterogeneous normalization strengths correlated with inhibitory currents.
- Reproduces experimental findings on spike count correlations dependence on normalization.
- Stronger normalization enhances sensitivity to contrast and information encoding efficiency.
Conclusions:
- Provides a mechanistic explanation for heterogeneous normalization strengths in the visual cortex.
- Neuronal heterogeneity in normalization is linked to inhibitory current dynamics.
- Increased normalization heterogeneity boosts information encoding in neural networks.
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