Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal...

Area of Science:

• Computational Neuroscience
• Neuroplasticity

Background:

• Synaptic strengths in neuronal networks adapt based on neuronal activity via spike-timing-dependent plasticity (STDP).
• Traditional STDP models often neglect propagation delays, limiting their ability to reproduce experimental findings and rule out bidirectional couplings.

Purpose of the Study:

• To review how extensions to classic STDP rules and realistic propagation patterns can reconcile theoretical models with experimental observations.
• To explore the impact of propagation delays and synaptic weight distributions on network connectivity.

Main Methods:

• Review of recent studies incorporating realistic propagation patterns and extended STDP rules.
• Analysis of bidirectional couplings in coupled oscillatory neurons and large networks.
• Investigation of the role of synaptic weight distribution's mean and standard deviation on emergent connectivity.

Main Results:

• Classic STDP with realistic propagation patterns successfully captures experimental findings.
• Bidirectional synapses can be preserved and potentiated in networks with propagation delays.
• Synaptic weight distribution (mean and standard deviation) and firing rates critically determine emergent structural connectivity, including loops and symmetry.

Conclusions:

• Incorporating propagation delays and considering synaptic weight distributions are essential for accurate modeling of neuronal plasticity.
• These findings have implications for understanding brain network dynamics and developing desynchronizing brain stimulation techniques.