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⚖️ Synaptic Scaling: The Brain's Homeostatic Plasticity Lab

Explore synaptic scaling, the homeostatic mechanism neurons use to globally adjust all their synapses up or down and keep firing rates stable, without erasing the relative differences that encode memories.

Neuroscience & Biophysics3DModerate60 FPS

This simulation demonstrates how a single neuron balances fast, synapse-specific Hebbian learning against slow, global homeostatic synaptic scaling. You can watch individual synaptic weights change from correlated firing while simultaneously observing the neuron track its own average firing rate and periodically renormalize all synapses together to keep that rate near a target setpoint, all while the relative pattern of strong and weak synapses is preserved.

🔬 What It Demonstrates

🎮 How to Use

Start by driving Hebbian learning between selected input synapses and the postsynaptic neuron to build up different synaptic weights, then observe the neuron's average firing rate drifting away from its setpoint. Trigger or allow homeostatic scaling to run and watch all synaptic weights adjust together multiplicatively. Compare the relative weight differences before and after scaling, and try disabling homeostatic scaling entirely to see the network drift toward runaway excitation or silence.

💡 Did You Know?

Did you know that TNF-alpha, the same signaling molecule your immune system uses to fight infection and drive inflammation, was found by Gina Turrigiano's lab to also be released by glial cells in the brain to help neurons scale their synapses upward after a period of low activity, showing that the nervous and immune systems share more molecular toolkit than once thought.

⚙ Under the hood

Explore synaptic scaling, the homeostatic mechanism that multiplicatively adjusts all of a neuron's synaptic weights to keep firing rates stable.

synaptic-scalinghomeostatic-plasticityneurosciencesynapsesneural-networksturrigianoampa-receptorslearning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install