The brain processes information through networks of neurons that fire in coordinated patterns. This simulation explores neural computation and emergent behavior.
About This Simulation
Build a dopaminergic reward prediction error model for reinforcement learning.
Key Concepts
- Neural Networks: Interconnected neurons that process information through synaptic connections, forming the basis of learning and memory.
- Hebbian Learning: "Neurons that fire together, wire together" - synaptic connections strengthen when pre and post-synaptic neurons are active simultaneously.
- Emergent Behavior: Complex behaviors arising from simple rules followed by many interacting agents or neurons.
- Oscillations & Rhythms: Coordinated neural activity patterns that underlie various brain states and cognitive functions.
Why It Matters
Neural modeling advances AI, brain-computer interfaces, and treatments for neurological disorders.
How to Explore
- Adjust the sliders to modify simulation parameters and observe how the system responds
- Look for emergent patterns that arise from agent interactions
- Try extreme parameter values to find phase transitions and tipping points
- Compare the simulation behavior to real-world phenomena
Category: Neuroscience & Behavior — Exploring neural systems and behavioral patterns