Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network

Area of Science:

• Computational Neuroscience
• Neuroscience
• Systems Neuroscience

Background:

• Random fluctuations are inherent in biological systems.
• Appropriate randomness can facilitate neural information transfer and memory encoding.
• Understanding the role of noise in neural processing is crucial.

Purpose of the Study:

• To investigate the effects of intrinsic random fluctuations on working memory tasks.
• To analyze how a spiking neuron-astrocyte network model responds to varying noise intensities.
• To explore the interplay between noise, astrocyte filtering, and synaptic plasticity in memory.

Main Methods:

• Development of a modified spiking neuron-astrocyte network model.
• Inclusion of excitatory-inhibitory balance, synaptic plasticity, and external input noise.
• Analysis of memory performance, signal transmission, and image recovery under different noise levels.

Main Results:

• Astrocyte networks act as low-pass filters, reducing noise and improving image recovery.
• Optimal random fluctuation intensity enhances memory performance; excessive noise inhibits it.
• Synaptic plasticity stabilizes working memory by reducing neuronal firing rates and peaks.

Conclusions:

• Random fluctuations play a critical role in working memory and neural information processing.
• Astrocyte filtering and synaptic plasticity are key mechanisms for managing noise in neural networks.
• The study provides insights into optimizing neural information processing through controlled randomness.