---
include_toc: true
---

# README.md

## Calcium wave

The calcium wave does not behave like a uniform broadcast. It has two distinct modes:

The first is a **local microdomain response**. When a single synapse fires heavily, the IP3 signal and the resulting calcium elevation can remain confined to the astrocyte process wrapping that specific synapse. The glutamine shuttle boost, the gliotransmitter release, and the EAAT upregulation all happen locally at that process tip — the rest of the astrocyte is unaffected. This is the normal case during moderate activity.

The second is a **propagating wave**. When activity is sustained and intense enough, the calcium signal spreads from process to process across the astrocyte body and even into neighbouring astrocytes via gap junctions. This is the threshold event that IP3 models. Once the wave propagates, the response is no longer selective — it affects multiple synapses under that astrocyte's territory.

### Implication for the model

Your current model simulates a single tripartite synapse — one presynapse, one postsynapse, one astrocyte process. In that context the selectivity question does not arise because there is only one synapse to serve.

But if you ever extend the model to multiple synapses under one astrocyte, the IP3 threshold becomes the selectivity mechanism. Below threshold, each synapse gets a local response proportional to its own activity. Above threshold — when the wave propagates — the astrocyte boosts all synapses in its territory, not just the active one. This is actually a form of network-level coordination: a heavily firing synapse can trigger a boost that benefits its neighbours, which may be important for synchronising activity across a local circuit.

The simplified model could capture this with a single parameter:

— selectivity rule:

- IP3 below threshold → boost applies only to this synapse (local)

- IP3 above threshold → boost applies to all synapses in territory (broadcast)

For a single-synapse model the distinction is invisible. But keeping the threshold concept in the model now means the architecture is already correct if you scale up later.
