Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Synaptic Plasticity

Background:

• Long-term modifications of neuronal connections are essential for memory storage.
• The location (pre- or postsynaptic) of these modifications is highly variable across different brain regions and synapse types.

Purpose of the Study:

• To develop a unifying theoretical framework for understanding the expression mechanisms and functional consequences of long-term synaptic plasticity.
• To explain the variable locus of expression (presynaptic vs. postsynaptic) in synaptic potentiation and depression.
• To elucidate the role of synaptic plasticity in maintaining excitatory-inhibitory balance.

Main Methods:

• Development of a theoretical model for synaptic plasticity.
• Mathematical analysis of postsynaptic response statistics optimization (mean and variance).
• Comparison of theoretical predictions with existing experimental data from hippocampal and neocortical synapses.

Main Results:

• The theory predicts that synaptic plasticity optimizes postsynaptic response statistics towards a specific mean with minimal variance.
• The timing of plasticity induction determines the ratio of pre- and postsynaptic modifications, explaining observed expression loci.
• The model accurately explains experimental findings for synaptic potentiation and predicts presynaptic expression for long-term depression.
• For inhibitory synapses, the theory suggests plasticity maintains excitatory-inhibitory balance by targeting mean excitation.

Conclusions:

• A unifying theory is proposed for long-term synaptic plasticity, explaining its expression locus and function.
• Synaptic plasticity serves to optimize neural response statistics for efficient information processing and memory.
• The framework provides insights into the regulation of synaptic strength and the maintenance of neural circuit stability.