NEST

NEST simulates spiking neural network models to investigate the dynamics, size, and structure of neural systems across scales, emphasizing network-level properties over individual neuron morphology.

Key Features:

• Versatility: Simulates networks of any size to support large-scale neural model investigations.
• Information processing models: Executes models of sensory processing such as the visual or auditory cortex of mammals.
• Network activity dynamics: Models laminar cortical networks and balanced random networks to study emergent activity patterns.
• Learning and plasticity: Supports models that explore learning mechanisms and neural plasticity.

Scientific Applications:

• Sensory cortex modeling: Investigates information processing in the visual or auditory cortex of mammals.
• Network dynamics analysis: Studies laminar cortical networks and balanced random networks to analyze emergent activity and stability.
• Learning and plasticity studies: Explores mechanisms of learning and synaptic plasticity within spiking network models.

Methodology:

Simulation of spiking neural network models that emphasize network-level dynamics, size, and structure rather than detailed neuronal morphology.