Table 1 Default parameters of the LIF network model
From: A Python toolbox for neural circuit parameter inference
Symbol | Value/definition | Description |
|---|---|---|
X | {E, I} | Population names |
NX ∈ {NE, NI} | {8192, 1024} | Population sizes |
CYX | 0.2 for all X, Y | Connection probability (pairwise Bernoulli, no autapses) |
CmX | {289.1, 110.7} pF | Membrane capacitance |
τm | 10 ms for all X | Membrane time constant |
RmX | τm/CmX | Membrane resistance |
EL | -65 mV for all X | Leak reversal potential |
Vθ | -55 mV for all X | Spike threshold |
Vr | EL for all X | Spike reset potential |
τr | 2 ms for all X | Refractory period |
\({{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle }\) | \({\rm{if}}{{\rm{V}}}_{{\rm{m}}}^{\left\langle {\rm{u}}\right\rangle }\left({{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle }\right)\ge {{\rm{V}}}_{{\rm{\theta }}}\) | Spike emission times; 〈u〉 is the neuron index |
\({{\rm{\tau }}}_{{\rm{m}}}\frac{{\rm{d}}{{\rm{V}}}_{{\rm{m}}}^{\left\langle {\rm{u}}\right\rangle }}{{\rm{dt}}}\) | \({-{\rm{V}}}_{{\rm{m}}}^{\left\langle {\rm{u}}\right\rangle }+{{\rm{R}}}_{{\rm{mX}}}{{\rm{I}}}_{{\rm{u}}}\left({\rm{t}}\right)\,{\rm{if}}\,\forall {\rm{k}},{\rm{t}}\,\notin \left({{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle },{{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle }+\left.{{\rm{\tau }}}_{{\rm{r}}}\right]\right.\) | Sub-threshold dynamics; Iu(t) is the synaptic activation current |
\({{\rm{V}}}_{{\rm{m}}}^{\left\langle {\rm{u}}\right\rangle }({\rm{t}})\) | \({{\rm{V}}}_{{\rm{r}}}{\rm{if}}\,{\rm{t}}\in \left({{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle },{{\rm{t}}}_{{\rm{k}}}^{\left\langle {\rm{u}}\right\rangle }+\left.{{\rm{\tau }}}_{{\rm{r}}}\right]\right.\) | Reset and refractoriness |
\({\bar{{\rm{J}}}}_{{\rm{synYX}}}\) | \(\left\{\begin{array}{c}1.589{\rm{nA\; for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{E}}\\ 2.020{\rm{nA\; for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{I}}\\ -23.84{\rm{nA\; for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ -8.441{\rm{nA\; for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Mean of the normal distribution that defines the maximum synaptic current of recurrent connections |
σ(JsynYX) | \(0.1\left|{\bar{{\rm{J}}}}_{{\rm{synYX}}}\right|\) | Standard deviation of the maximum synaptic current |
\({{\rm{\tau }}}_{{\rm{syn}}}^{{\rm{exc}}}\), \({{\rm{\tau }}}_{{\rm{syn}}}^{{\rm{inh}}}\) | 0.5 ms | Exp. syn. decay time constant |
\({\bar{\Delta }}_{{\rm{YX}}}\) | \(\left\{\begin{array}{c}2.520{\rm{ms\; for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{E}}\\ 1.714{\rm{ms\; for}}{\rm{X}}={\rm{E}},{\rm{Y}}={\rm{I}}\\ 1.585{\rm{ms\; for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{E}}\\ 1.149{\rm{ms\; for}}{\rm{X}}={\rm{I}},{\rm{Y}}={\rm{I}}\end{array}\right.\) | Mean of the normal distribution that defines conduction delays of recurrent connections |
σ(ΔYX) | \(0.5{\bar{\Delta }}_{{\rm{YX}}}\) | Standard deviation of conduction delays |
\({{\rm{J}}}_{{\rm{syn}}}^{{\rm{ext}}}\) | 29.89 nA | Max. synaptic current of ext. input |
\({{\rm{k}}}_{{\rm{Y}}}^{{\rm{ext}}}\) | {465, 160} | Ext. synapses per neuron |
\(\left\langle {{\rm{\upsilon }}}_{{\rm{ext}}}\right\rangle\) | 40 s-1 (Poisson statistics) | Ext. syn. activation rate |
