Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. I: Spike...

Area of Science:

• Computational Neuroscience
• Systems Neuroscience

Background:

• Synaptic plasticity, crucial for neural function, is driven by spike trains.
• Spike train variability (spatial randomness, temporal stochasticity) leads to synaptic efficacy variability.
• The precise influence of spike train variability on synaptic efficacy variability remains poorly understood.

Purpose of the Study:

• To investigate how spike train statistical features impact synaptic efficacy variability.
• To analyze this influence under pair-wise additive spike-timing dependent plasticity (STDP) with synaptic homeostasis.
• To elucidate the mechanisms behind efficacy variability, separating drift and diffusion components.

Main Methods:

• Analytical and numerical studies of converging motifs (one neuron receiving input from many).
• Utilized statistical neuron models with tunable parameters to control spike pattern statistics.
• Separated efficacy variability into drift (heterogeneity of change rates) and diffusion (weight diffusion due to stochasticity).

Main Results:

• Synchronous firing and burstiness tend to increase the diffusion component (DiffV).
• Heterogeneity of firing rates induces the drift component (DriftV) when STDP potentiation/depression are unbalanced.
• Heterogeneity of cross-correlations, combined with rate heterogeneity, also induces DriftV.

Conclusions:

• Spike train characteristics significantly shape synaptic efficacy variability.
• Understanding these relationships is vital for comprehending neural network functions like memory and neural development.
• The study provides a framework for dissecting variability contributions in neural systems.