Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis.

Area of Science:

• Computational Neuroscience
• Developmental Neuroscience
• Systems Neuroscience

Background:

• Existing models of neuronal development often rely on extreme genetic or environmental determinism.
• Current models of synaptogenesis and synaptic modification, frequently based on Hebbian rules, can lead to morphogenetic instability.
• A gap exists in understanding how neuronal networks achieve stable morphogenesis while maintaining dynamic activity patterns.

Purpose of the Study:

• To propose and evaluate a moderate approach to ontogenesis, integrating pre-established neuronal networks with activity-dependent feedback.
• To formalize the compensation theory of synaptogenesis into a computational algorithm.
• To investigate the capacity of this algorithm to generate morphogenetically stable neuronal networks capable of self-maintained activity oscillations.

Main Methods:

• Application of a novel algorithm, based on the compensation theory of synaptogenesis, to randomly connected McCulloch-Pitts networks.
• Simulation of network dynamics to observe the effects of the compensation algorithm on activity patterns and stability.
• Analysis of structural and functional properties resulting from the compensation-driven process.

Main Results:

• The compensation algorithm successfully generated morphogenetically stable neuronal networks.
• These networks preserved self-maintained oscillations in activity, a feature lacking in standard Hebbian models without further assumptions.
• The algorithm demonstrated the selective stabilization and elimination of synapses, leading to stable network structures from random initial connectivity.

Conclusions:

• The compensation theory of synaptogenesis provides a viable mechanism for developing morphogenetically stable neuronal networks from random connectivity.
• This theory can account for both synaptic population behavior and complex network-level interactions, including self-organization at the individual neuron level.
• The developed algorithm offers a new perspective on neuronal development, reconciling stability with dynamic activity through a feedback-driven compensation process.