Neuron Threshold Sandbox

Explore a simplified decision boundary

A real neuron integrates signals across space and time through electrochemical mechanisms. This lab deliberately does not simulate those mechanisms. Instead, it lets you move two abstract sliders: an excitatory contribution and an inhibitory contribution. The toy rule subtracts the second from the first and compares the result to a threshold of four arbitrary units.

When the net input meets or exceeds four, the model reports a signal. Below four, it reports no signal. Observe how changing one unit can flip the result near the boundary. The purpose is to understand what a threshold rule does in a simplified model.

Try three investigations

First, hold inhibition at zero and increase excitation until the signal appears. Second, keep excitation fixed and increase inhibition. Third, select two different input pairs that produce the same net result. Explain how the model treats them similarly and why that would not fully capture a real neuron.

Scientific boundary

These numbers are invented teaching units, not millivolts, actual synaptic strengths, or clinical measurements. Real action potentials involve voltage-gated channels, membrane properties, timing and numerous interacting processes. Do not interpret the output as biological prediction or medical advice.

See the OpenStax chapters for the electrochemical foundations that this deliberately simple demonstration leaves out.