Simulations of working memory spiking networks driven by short-term plasticity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Science

Background:

• Working Memory (WM) is crucial for temporary information processing in the brain.
• WM is associated with sustained neuronal activity following external cues.
• Existing models often focus on persistent firing rather than synaptic mechanisms.

Purpose of the Study:

• To implement and investigate a spiking neural network model for Working Memory.
• To explore the role of short-term synaptic facilitation in sustaining WM activity.
• To determine if synaptic mechanisms can support the storage and maintenance of multiple memories.

Main Methods:

• Utilized the NEST simulator to create a spiking neural network model.
• Incorporated leaky integrate-and-fire neurons with exponential postsynaptic currents.
• Modeled WM activity based on presynaptic calcium kinetics and short-term synaptic facilitation.

Main Results:

• The model autonomously exhibited an activity regime where memory information was stored synaptically via facilitation.
• Spiking activity was observed to be functional in maintaining synaptic facilitation.
• The network successfully maintained multiple memories through alternated synchronous activity, preserving synaptic facilitation.

Conclusions:

• Synaptic facilitation, driven by presynaptic calcium kinetics, can effectively sustain Working Memory.
• This synaptic mechanism offers a viable alternative to persistent neuronal firing for WM.
• The model provides insights into the neural basis of information storage and retrieval in WM.