Formation and maintenance of neuronal assemblies through synaptic plasticity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• The cerebral cortex exhibits plasticity, enabling synaptic connectivity to store sensory information.
• Maintaining these synaptic patterns amidst high neuronal spike variability remains a challenge.

Purpose of the Study:

• To investigate how neuronal assemblies are formed and maintained in cortical networks.
• To determine the role of synaptic plasticity and homeostatic mechanisms in stabilizing learned network architectures.
• To explore the impact of spontaneous and evoked neural activity on learned structures.

Main Methods:

• Utilized a large-scale computational model of cortical networks.
• Incorporated realistic synaptic plasticity rules and homeostatic mechanisms.
• Analyzed spontaneous and evoked spiking activity to identify signatures of learned assemblies.

Main Results:

• Demonstrated the formation of neuronal assemblies reflecting prior stimuli through plasticity and homeostatic regulation.
• Showed that reverberation of past activity in spontaneous neural firing stabilizes learned synaptic patterns.
• Identified specific signatures within neural activity that indicate learned assembly structures.

Conclusions:

• Realistic synaptic plasticity rules, coupled with homeostatic mechanisms, are crucial for forming and maintaining learned cortical architectures.
• Spontaneous neural activity plays a stabilizing role in preserving memory traces within the brain.
• The study provides testable predictions regarding the influence of recent sensory experiences on neural firing patterns.