This is an attempt at a primitive model of signaling in the central nervous system.

The system consists of 16 "neurons", labeled 0 - f. Each neuron at any given time has a state of 0 - 255. The nominal and initial state of each neuron (resting potential) is 128.

Neurons connect to dependent neurons (axon terminals => dendrites) in a specified manner (the network). This corresponds to the data structure known as a directed graph, either cyclic or acyclic The connection can be inhibitory or excitatory (postsynaptic potential). Signal magnitude varies from -127 to 128. The Network Table displays the connection. To illustrate, look at column 0 in that table. It indicates that neuron 0a sends an inhibitory signal of -27 to neuron 2 and an excitatory signal of 69 to neuron 3.

When the state of a neuron becomes greater than 255 (by receiving signals from predecessor neurons), the neuron activates (action potential) and sends signals to its dependents, (synaptic transmission), following it returns to the resting state (128). You may start the process by clicking any of the ⮟ buttons, which will create an action potential in the corresponding neuron and propagate the signal through the system.

Please note that if the graph is cyclic (activation can propagate back to the activated neuron), you can cause an infinite loop. In a real central nervous system, that would be a disaster. Here, hopefully your browser will take care of it.

There are obvious limitations to this model. In an actual nervous system, there is plasticity - signal strength can vary based on internal state and influences from other neurons. Also, the model allows for spatial summation (activation is the sum of signals received at a given time) but not for temporal summation (the sum of signals received at different times). And neurons have refractory periods - after activation, they cannot reactivate for a given period, which also can vary.