One nose but two nostrils: Learn to align with sparse connections between two olfactory cortices

Area of Science:

• Neuroscience
• Computational Neuroscience
• Artificial Intelligence

Background:

• Inter-hemispheric integration is crucial for brain function.
• Odor responses in cortical neurons show high correlation between nostrils, suggesting structured connections.
• The precise mechanisms of this bilateral alignment remain unclear.

Purpose of the Study:

• To investigate how continuous odor exposure shapes inter-hemispheric neural projections.
• To model this process using online learning with a local Hebbian rule.
• To compare Hebbian learning with global stochastic gradient descent (SGD) for artificial neural networks.

Main Methods:

• Modeled neural projection shaping as online learning with a local Hebbian rule.
• Analyzed the trade-off between speed and accuracy in bilateral alignment.
• Identified scaling relationships between neuron count and projection density.
• Compared Hebbian learning with SGD in artificial neural networks.

Main Results:

• Hebbian learning with sparse connections effectively achieves bilateral alignment.
• A linear trade-off between learning speed and alignment accuracy was observed.
• An inverse scaling relationship exists: more cortical neurons permit sparser inter-hemispheric projections for accurate alignment.
• SGD achieved similar alignment accuracy with sparser connectivity, following the same inverse scaling.

Conclusions:

• Continuous odor exposure can shape inter-hemispheric connections via Hebbian learning.
• Sparse connectivity and Hebbian learning are efficient mechanisms for achieving bilateral neural alignment.
• The alignment mechanisms of Hebbian learning and SGD are fundamentally similar due to aligned update vectors.
• Findings may inform the development of efficient sparse local learning algorithms for complex computational problems.