Dynamics and plasticity of stimulus-selective persistent activity in cortical network models

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Neuroscience

Background:

• Persistent neuronal activity is crucial for cognitive tasks involving working memory.
• Understanding the mechanisms sustaining this activity in cortical circuits is a key challenge.

Purpose of the Study:

• To review and analyze computational models of persistent neuronal activity.
• To explore how associative learning modifies persistent activity in cortical networks.
• To validate theoretical models against neurophysiological recordings.

Main Methods:

• Review of basic models: bistable (excitation-only) and multistable (structured excitation, global inhibition).
• Incorporation of Hebbian learning rules to shape synaptic plasticity.
• Comparison of model predictions with experimental data from monkey neurophysiology.

Main Results:

• Models demonstrate how persistent activity can be sustained in cortical circuits.
• Hebbian learning dynamically alters synaptic structures, impacting persistent activity properties.
• Theoretical models successfully replicate experimental findings from specific cognitive tasks.

Conclusions:

• Computational models provide valuable insights into the neural basis of working memory.
• Associative learning plays a significant role in modulating persistent neuronal activity.
• The interplay between network structure and learning shapes cognitive functions.