Table 4 Synaptic connectivity of the multicompartment network model
From: A Python toolbox for neural circuit parameter inference
Symbol | Value/definition | Description |
|---|---|---|
\({\bar{{\rm{G}}}}_{{\rm{synYX}}}\) | \(\left\{\begin{array}{l}0.15{\rm{nS}}\; {\rm{for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{E}}\\ 0.125{\rm{nS}}\; {\rm{for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{I}}\\ 4.5{\rm{nS}}\; {\rm{for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ 2.0{\rm{nS}}\; {\rm{for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Mean of the normal distribution that defines the maximum synaptic conductance of recurrent connections |
σ(GsynYX) | \(0.1{\bar{{\rm{G}}}}_{{\rm{synYX}}}\) | Standard deviation of the maximum synaptic conductance |
CYX | 0.05 for all X, Y | Connection probability (pairwise Bernoulli, no autapses) |
EsynX | 0 mV for X = E, -80 mV for X = I | Synaptic reversal potential |
fYX (t) | \(\left(\frac{{{\rm{e}}}^{-({\rm{t}}-{{\rm{t}}}_{{\rm{s}}})/{{\rm{\tau }}}_{1}}-{{\rm{e}}}^{-({\rm{t}}-{{\rm{t}}}_{{\rm{s}}})/{{\rm{\tau }}}_{2}}}{{{\rm{e}}}^{-{{\rm{\tau }}}_{{\rm{peak}}}/{{\rm{\tau }}}_{1}}-{{\rm{e}}}^{-{{\rm{\tau }}}_{{\rm{peak}}}/{{\rm{\tau }}}_{2}}}\right){\rm{where}}\) \({{\rm{\tau }}}_{{\rm{peak}}}=\frac{{{\rm{\tau }}}_{2}{{\rm{\tau }}}_{1}}{{{\rm{\tau }}}_{2}-{{\rm{\tau }}}_{1}}\log \left(\frac{{{\rm{\tau }}}_{2}}{{{\rm{\tau }}}_{1}}\right)\) | Synaptic temporal kernel |
τ1 | 0.2 ms for X = E, 0.1 ms for X = I | Synaptic rise time constant |
τ2 | 1.8 ms for X = E, 9.0 ms for X = I | Synaptic decay time constant |
\({\bar{\Delta }}_{{\rm{YX}}}\) | \(\left\{\begin{array}{c}1.5{\rm{ms\; for\; X}}={\rm{E}},{\rm{Y}}={\rm{E}}\\ 1.4{\rm{ms\; for\; X}}={\rm{E}},{\rm{Y}}={\rm{I}}\\ 1.3{\rm{ms\; for\; X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ 1.2{\rm{ms\; for\; X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Mean of the truncated normal distribution that defines the conduction delay (truncated at 0.3 ms) |
σ(ΔYX) | \(\left\{\begin{array}{c}0.3{\rm{ms\; for\; X}}={\rm{E}},{\rm{Y}}={\rm{E}}\\ 0.4{\rm{ms\; for\; X}}={\rm{E}},{\rm{Y}}={\rm{I}}\\ 0.5{\rm{ms\; for\; X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ 0.6{\rm{ms\; for\; X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Standard deviation of conduction delays |
LYX (z) | \(\left\{\begin{array}{l}\begin{array}{c}\frac{{\mathcal{N}}\left(0,100\right)}{2}{\mathcal{+}}{\mathcal{N}}\left(500,100\right),{\rm{for}}\; {\rm{X}}={\rm{E}},{\rm{Y}}={\rm{E}},\,{\mathcal{S}}\,\{{\rm{soma}}\}\,\\ \,\end{array}\\ {\mathcal{N}}\left(50,100\right){\rm{for}}\; {\rm{X}}={\rm{E}},{\rm{Y}}={\rm{I}},\,{\mathcal{S}}\,\{{\rm{soma}}\}\\ {\mathcal{N}}\left(-50,100\right){\rm{for}}\; {\rm{X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ {\mathcal{N}}\left(-100,100\right){\rm{for}}\; {\rm{X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Normal distributions that define synaptic density in the z-axis |
\({\bar{{\rm{G}}}}_{{\rm{synYext}}}\) | 0.2 nS | External synapse conductance |
Eext | 0 mV | Ext. synapse rev. potential |
fYext (t) | fYX (t) | Ext. synapse temporal kernel |
τ1 | 0.2 ms | Ext. synapse rise time constant |
τ2 | 1.8 ms | Ext. synapse decay time constant |
kYext | {465, 160} | Number of ext. synapses per neuron |
\(\left\langle {{\rm{\upsilon }}}_{{\rm{ext}}}\right\rangle\) | 40 s−1 (Poisson statistics) | Ext. syn. activation rate |
\({\bar{\Delta }}_{{\rm{Yext}}}\) | δ(t) | Ext. syn. conduction delay |
LYext | 1 | Ext. syn. depth dependence |
