Is there some kind of handbook for parameter values in brain modeling available in Brian?

Hi. To set the equations for different types of neurons as well as synaptic weights, delays and probability of connections I spend hours going through various articles. Is there some kind of handbook for this which we may use during modeling? Or is there some built-in parameters related to this inside Brian?
Specifically, I am modeling three types of neuron in neocortex (considering all to be one-compartmental and using Hodgkin-Huxley model): pyramidals (PY), parvalbumins (PV), somatostatins (SST). I wrote the equations for each type (which I’m not sure if they exactly reflect the nature of these neurons). I’m not sure about what range of values to use for some of the parameters in the equations and I don’t know what probabilities I should use for synapse connections and what range of values to use for synaptic weights and delays. Also, I’m not sure about the existence of connection between these types. One article says except SST-SST, all the others can be connected. The other says, there is no connections between PV and SST.
I would really appreciate it if someone could help me in this regard. Thank you.

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One place to start looking would be the Brian page of ModelDB [here]
[and another Brian2 model list]

and if you can’t find parameters for your proposed setup there, zoom out to the rest of ModelDB [here] where you can search by brain region or cell type.
You may be able to find an implementation for a different simulator that you could port over to Brian.

Another place to look would be the Brian2 examples page. These are nice because you can quickly launch them with Binder.

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I’ve done a quick search for PV, SST, Pyr circuits simulated in Brian2, and these might be relevant:

A circuit model of auditory cortex” Park & Geffen

The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity
in a Full-Scale Spiking Network Model
” Potjans & Diesmann

  • more recent Brian implementation [here]
  • has intricate between-layer connectivity params, but doesn’t subdivide inhibitory into parvalbumin v.s. somatostatin

Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning

" Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms" Ferguson et al.

  • (can’t find the code)

" Divisive gain modulation enables flexible and rapid entrainment in a neocortical microcircuit model" Papasavvas et al.

  • a MATLAB implementation including Pyr, PV, SST, but as a rate-model rather than biophysical per-neuron code here
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Thank you very much adam. This is great. Thanks for the suggestions as well

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