🧠 Neural Network Lab
Neural Network Simulation: How the Brain Learns
This neural network simulation models how interconnected populations of neurons process inputs, integrate signals, and modify connection strengths through learning rules analogous to synaptic plasticity. Students explore how network architecture — number of layers, connectivity patterns, excitatory and inhibitory balance — determines what kinds of patterns a network can detect and remember. Unlike the single-neuron simulation, this tool examines emergent computation at the network level, bridging biology and the principles underlying brain function.
What you can do in this simulation
- Build networks of model neurons and connect them with excitatory and inhibitory synapses
- Apply Hebbian learning rules to strengthen connections between co-active neurons
- Present repeated inputs and watch the network develop stable response patterns
- Adjust excitation-inhibition balance and observe how it affects network stability and oscillations
- Visualize activity propagating across layers to understand hierarchical information processing
Concepts covered
neural network · synaptic plasticity · Hebbian learning · excitatory and inhibitory neurons · network computation · computational neuroscience
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