Network bursts in cortical cultures are best simulated using pacemaker neurons and adaptive synapses

Area of Science:

• Computational Neuroscience
• Neural Network Modeling
• Systems Neuroscience

Background:

• Dissociated cultured neural networks (NNs) exhibit synchronized bursting, a key feature of their electrical activity.
• Burst profiles in biological NNs vary significantly in shape, width, and firing rate.
• Existing models often rely on noise injection to induce bursting, which may not fully replicate biological phenomena.

Purpose of the Study:

• To reproduce the specific features of synchronized bursts observed in biological neural networks.
• To investigate the role of pacemaker-like neurons in generating realistic burst profiles.
• To analyze the influence of network parameters on burst characteristics.

Main Methods:

• Developed random connectivity network models with 5,000 neurons.
• Compared noise injection with the introduction of a small subset of pacemaker neurons.
• Incorporated adaptive synapses into the network models.
• Quantitatively analyzed the impact of network connectivity, transmission delays, and excitatory fraction.

Main Results:

• Noise injection produced bursts with an unrealistically gentle rising slope.
• Pacemaker neurons successfully triggered bursts with more realistic profiles.
• The combination of pacemaker neurons and adaptive synapses yielded burst features (shape, width, height) comparable to experimental data.
• Network parameters like connectivity, delays, and excitatory fraction were shown to quantitatively influence burst features.

Conclusions:

• Pacemaker-like neurons are crucial for generating biologically realistic synchronized bursts in neural network models.
• Adaptive synapses further enhance the realism of simulated burst dynamics.
• Computational models incorporating these features provide valuable insights into the organization and behavior of biological neural networks.