Week 2: Modeling the Glomerulus

Last week we were concerned with modeling a single neuron, but this week we need to focus on connecting these neurons together. Neurons in the Antennal Lobe are organized into structures called glomeruli.

 

The glomerulus is the major subunit of the Antennal Lobe. Glomeruli are composed of both projection neurons (PNs) and local neurons (LNs) . In our model, each glomerulus will contain 10 PNs and 6 LNs. The primary focus of this week will be to connect the neurons together, so we must consider two separate cases: intraglomerular connections and interglomerular connections.

 

Intraglomerular connections refer to the connections where one neuron within a glomerulus synapses onto another neuron within the same glomerulus. In the model, these connections were determined randomly, where PNs had a 75% chance of synapsing onto another PN or LN within their own glomerulus. LNs, on the other hand, had a 38% chance of synapsing onto another PN within their glomerulus, and only a 25% chance of synapsing onto another LN within their own glomerulus.

 

Interglomerular connections are rather different. For starters, PNs do not have any interglomerular connections. On the other hand, LNs are able to synapse onto PNs in another glomerulus with a 55% chance, but they do not synapse onto LNs in other glomeruli at all. This makes interglomerular connections sparse, but nonetheless invaluable to obtaining proper neuron dynamics.

 

With the glomerular structure now established, we can finally truly test the neurons. Shown below is an example figure illustrating the dynamics of a single neuron. Please note that voltage is normalized, and thus a voltage of 0 represents resting potential, and a voltage of 1 represents firing threshold.

 

ExamplePN

With the glomerular subunit now established, next week will consist of creating the entire Antennal Lobe.  The Antennal Lobe itself will be a piece of code that creates all of the other subunits, and also helps to retrieve the information that we wish to obtain from the model.

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