Heterogeneous Responses to Changes in Inhibitory Synaptic Strength in Networks of Spiking Neurons

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding synaptic plasticity is crucial for comprehending neural network dynamics.
• Spontaneous activity in neuronal networks is fundamental to brain function.
• Synaptic weights significantly shape network behavior and information processing.

Purpose of the Study:

• To investigate the impact of altered inhibitory synaptic weights on the spontaneous activity of spiking neural networks.
• To explore the heterogeneity of neuronal responses to changes in synaptic inhibition.
• To identify network structural features that influence responses to synaptic modifications.

Main Methods:

• Numerical simulations of neural networks with conductance-based synapses.
• Utilizing biologically realistic network models reconstructed from multi-electrode array recordings.
• Systematically altering inhibitory synaptic weights and observing network dynamics.

Main Results:

• Neuronal responses to uniform decreases or increases in inhibitory synaptic weights were heterogeneous, with firing rates increasing, decreasing, or remaining unchanged.
• Heterogeneous responses suggest that synaptic modifications do not always create positive feedback loops in network dynamics.
• A long-tailed distribution of average outgoing synaptic weights was identified as crucial for network bursting and overall response to inhibition changes.

Conclusions:

• The distribution of synaptic weights, particularly a long-tailed distribution, is a key determinant of network bursting and response patterns.
• Biologically realistic network models are essential for accurately predicting responses to synaptic plasticity.
• Findings offer insights into the effects of pharmacological agents like bicuculline on neuronal activity.