#manim

messing with manim :)

In short, the beads on the edges represent the load an edge gives to each of its nodes. the total load of every edge is 1, and what I am trying to do with this algorithm is to distribute the load evenly.

I am adding another level of indirection here, by adding levels to the nodes: a node with a high load increases its level, and when it has a much higher level than its neighbors it starts giving them some of its load.

so i built a simulation of schizophrenia using rust and python

basically you have two groups of simulated neurons, one inhibitory and one excitatory. the excitatory group is connected so they will settle on one specific pattern. the inhibitory group is connected to the excitatory group semi-randomly. the excitatory group releases glutamate while the inhibitory group releases gaba. glutamate will cause the neurons to increase in voltage (or depolarize), gaba will cause the neurons to decrease in voltage (hyperpolarize).

heres a quick visualization of the results in manim

the y axis represents the average firing rate of the excitatory group over time, decay refers to how quickly glutamate is cleared from the neuronal synapse. there are two versions of the simulation, one where the excitatory group is presented with a cue, and one where it is not presented with a cue. when the cue is present, the excitatory group remembers the pattern and settles on it, represented by an increased firing rate. however, not every trial in the simulation leads to a memory recall, if the glutamate clearance happens too quickly, the memory is not maintained. on the other hand, when no cue is presented if glutamate clearance is too low, spontaneous activity overcomes inhibition and activity persists despite there being no input, ie a hallucination.