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fix sphinx warnings
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jessica-mitchell committed Aug 6, 2024
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2 changes: 1 addition & 1 deletion doc/htmldoc/synapses/index.rst
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Expand Up @@ -9,7 +9,7 @@ Guides on using synapses in NEST

.. grid:: 1 1 2 2

.. grid-item-card:: Managing coonnections
.. grid-item-card:: Managing coonnections

* :ref:`connectivity_concepts`
* :ref:`connection_generator`
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34 changes: 21 additions & 13 deletions models/iaf_bw_2001.h
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Expand Up @@ -88,7 +88,7 @@ The membrane potential and synaptic variables evolve according to
I_\mathrm{NMDA} &= \frac{(V(t) - V_E)}{1+[\mathrm{Mg^{2+}}]\mathrm{exp}(-0.062V(t))/3.57}\sum_{j \in \Gamma_\mathrm{ex}}^{N_E}w_jS_{j,\mathrm{NMDA}}(t) \\[3ex]
I_\mathrm{GABA} &= (V(t) - V_I)\sum_{j \in \Gamma_\mathrm{in}}^{N_E}w_jS_{j,\mathrm{GABA}}(t) \\[5ex]
\frac{dS_{j,\mathrm{AMPA}}}{dt} &= -\frac{j,S_{\mathrm{AMPA}}}{\tau_\mathrm{AMPA}}+\sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{GABA}}}{dt} &= -\frac{S_{j,\mathrm{GABA}}}{\tau_\mathrm{GABA}} + \sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{GABA}}}{dt} &= -\frac{S_{j,\mathrm{GABA}}}{\tau_\mathrm{GABA}} + \sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{NMDA}}}{dt} &= -\frac{S_{j,\mathrm{NMDA}}}{\tau_\mathrm{NMDA,decay}} + \sum_{k \in \Delta_j} (k_0 + k_1 S(t)) \delta (t - t_j^k) \\[3ex]
where :math:`\Gamma_\mathrm{ex}` and :math:`\Gamma_\mathrm{in}` are index sets for presynaptic excitatory and inhibitory neurons respectively, and :math:`\Delta_j` is an index set for the spike times of neuron :math:`j`.
Expand All @@ -105,6 +105,8 @@ The specification of this model differs slightly from the one in [1]_. The param
:math:`g_\mathrm{GABA}`, and :math:`g_\mathrm{NMDA}` have been absorbed into the respective synaptic weights.
Additionally, the synapses from the external population are not separated from the recurrent AMPA-synapses.
See also [2]_ and [3]_.
For more implementation details and a comparison to the exact version, see:
- `Brunel_Wang_2001_Model_Approximation <../model_details/Brunel_Wang_2001_Model_Approximation.ipynb>`_
Expand All @@ -118,16 +120,16 @@ The following parameters can be set in the status dictionary.
**Parameter** **Default** **Math equivalent** **Description**
=================== ================== ================================= ========================================================================
``E_L`` -70.0 mV :math:`E_\mathrm{L}` Leak reversal potential
``E_ex`` 0.0 mV :math:`E_\mathrm{ex}` Excitatory reversal potential
``E_in`` -70.0 mV :math:`E_\mathrm{in}` Inhibitory reversal potential
``V_th`` -55.0 mV :math:`V_\mathrm{th}` Spike threshold
``E_ex`` 0.0 mV :math:`E_\mathrm{ex}` Excitatory reversal potential
``E_in`` -70.0 mV :math:`E_\mathrm{in}` Inhibitory reversal potential
``V_th`` -55.0 mV :math:`V_\mathrm{th}` Spike threshold
``V_reset`` -60.0 mV :math:`V_\mathrm{reset}` Reset potential of the membrane
``C_m`` 250.0 pF :math:`C_\mathrm{m}` Capacitance of the membrane
``g_L`` 25.0 nS :math:`g_\mathrm{L}` Leak conductance
``t_ref`` 2.0 ms :math:`t_\mathrm{ref}` Duration of refractory period
``C_m`` 250.0 pF :math:`C_\mathrm{m}` Capacitance of the membrane
``g_L`` 25.0 nS :math:`g_\mathrm{L}` Leak conductance
``t_ref`` 2.0 ms :math:`t_\mathrm{ref}` Duration of refractory period
``tau_AMPA`` 2.0 ms :math:`\tau_\mathrm{AMPA}` Time constant of AMPA synapse
``tau_GABA`` 5.0 ms :math:`\tau_\mathrm{GABA}` Time constant of GABA synapse
``tau_rise_NMDA`` 2.0 ms :math:`\tau_\mathrm{NMDA,rise}` Rise time constant of NMDA synapse
``tau_rise_NMDA`` 2.0 ms :math:`\tau_\mathrm{NMDA,rise}` Rise time constant of NMDA synapse
``tau_decay_NMDA`` 100.0 ms :math:`\tau_\mathrm{NMDA,decay}` Decay time constant of NMDA synapse
``alpha`` 0.5 ms^{-1} :math:`\alpha` Rise-time coupling strength for NMDA synapse
``conc_Mg2`` 1.0 mM :math:`[\mathrm{Mg}^+]` Extracellular magnesium concentration
Expand All @@ -140,9 +142,9 @@ The following state variables evolve during simulation and are available either
**State variable** **Initial value** **Math equivalent** **Description**
================== ================= ========================== =================================
``V_m`` -70 mV :math:`V_{\mathrm{m}}` Membrane potential
``s_AMPA`` 0 :math:`s_\mathrm{AMPA}` AMPA gating variable
``s_GABA`` 0 :math:`s_\mathrm{GABA}` GABA gating variable
``s_NMDA`` 0 :math:`s_\mathrm{NMDA}` NMDA gating variable
``s_AMPA`` 0 :math:`s_\mathrm{AMPA}` AMPA gating variable
``s_GABA`` 0 :math:`s_\mathrm{GABA}` GABA gating variable
``s_NMDA`` 0 :math:`s_\mathrm{NMDA}` NMDA gating variable
``I_NMDA`` 0 pA :math:`I_\mathrm{NMDA}` NMDA current
``I_AMPA`` 0 pA :math:`I_\mathrm{AMPA}` AMPA current
``I_GABA`` 0 pA :math:`I_\mathrm{GABA}` GABA current
Expand Down Expand Up @@ -170,8 +172,14 @@ SpikeEvent, CurrentEvent, DataLoggingRequest
References
++++++++++
.. [1] Wang, X.-J. (1999). Synaptic Basis of Cortical Persistent Activity: The Importance of NMDA Receptors to Working Memory. Journal of Neuroscience, 19(21), 9587–9603. https://doi.org/10.1523/JNEUROSCI.19-21-09587.1999
.. [2] Brunel, N., & Wang, X.-J. (2001). Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition. Journal of Computational Neuroscience, 11(1), 63–85. https://doi.org/10.1023/A:1011204814320
.. [1] Wang, X.-J. (1999). Synaptic Basis of Cortical Persistent Activity: The
Importance of NMDA Receptors to Working Memory. Journal of Neuroscience,
19(21), 9587–9603. https://doi.org/10.1523/JNEUROSCI.19-21-09587.1999
.. [2] Brunel, N., & Wang, X.-J. (2001). Effects of Neuromodulation in a Cortical
Network Model of Object Working Memory Dominated by Recurrent Inhibition.
Journal of Computational Neuroscience, 11(1), 63–85. https://doi.org/10.1023/A:1011204814320
.. [3] Wang, X. J. (2002). Probabilistic decision making by slow reverberation in
cortical circuits. Neuron, 36(5), 955-968. https://doi.org/10.1016/S0896-6273(02)01092-9
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24 changes: 13 additions & 11 deletions models/iaf_bw_2001_exact.h
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Expand Up @@ -87,7 +87,7 @@ The membrane potential and synaptic variables evolve according to
I_\mathrm{NMDA} &= \frac{(V(t) - V_E)}{1+[\mathrm{Mg^{2+}}]\mathrm{exp}(-0.062V(t))/3.57}\sum_{j \in
\Gamma_\mathrm{ex}}^{N_E}w_jS_{j,\mathrm{NMDA}}(t) \\[3ex]
I_\mathrm{GABA} &= (V(t) - V_I)\sum_{j \in \Gamma_\mathrm{in}}^{N_E}w_jS_{j,\mathrm{GABA}}(t) \\[5ex]
\frac{dS_{j,\mathrm{AMPA}}}{dt} &=-\frac{j,S_{\mathrm{AMPA}}}{\tau_\mathrm{AMPA}}+\sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{AMPA}}}{dt} &=-\frac{j,S_{\mathrm{AMPA}}}{\tau_\mathrm{AMPA}}+\sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{GABA}}}{dt} &= -\frac{S_{j,\mathrm{GABA}}}{\tau_\mathrm{GABA}} + \sum_{k \in \Delta_j} \delta (t - t_j^k) \\[3ex]
\frac{dS_{j,\mathrm{NMDA}}}{dt} &= -\frac{S_{j,\mathrm{NMDA}}}{\tau_\mathrm{NMDA,decay}}+ \alpha x_j (1 - S_{j,\mathrm{NMDA}})\\[3ex]
\frac{dx_j}{dt} &= -\frac{x_j}{\tau_\mathrm{NMDA,rise}} + \sum_{k \in \Delta_j} \delta (t - t_j^k)
Expand All @@ -106,6 +106,8 @@ The specification of this model differs slightly from the one in [1]_. The param
Additionally, the synapses from the external population is not separated from the recurrent AMPA-synapses.
This model is slow to simulate when there are many neurons with NMDA-synapses, since each post-synaptic neuron simulates each pre-synaptic connection explicitly. The model :doc:`iaf_bw_2001 </models/iaf_bw_2001>` is an approximation to this model which is significantly faster.
See also [2]_, [3]_
Parameters
++++++++++
Expand All @@ -115,16 +117,16 @@ The following parameters can be set in the status dictionary.
**Parameter** **Default** **Math equivalent** **Description**
=================== ================== ================================= ========================================================================
``E_L`` -70.0 mV :math:`E_\mathrm{L}` Leak reversal potential
``E_ex`` 0.0 mV :math:`E_\mathrm{ex}` Excitatory reversal potential
``E_in`` -70.0 mV :math:`E_\mathrm{in}` Inhibitory reversal potential
``V_th`` -55.0 mV :math:`V_\mathrm{th}` Spike threshold
``E_ex`` 0.0 mV :math:`E_\mathrm{ex}` Excitatory reversal potential
``E_in`` -70.0 mV :math:`E_\mathrm{in}` Inhibitory reversal potential
``V_th`` -55.0 mV :math:`V_\mathrm{th}` Spike threshold
``V_reset`` -60.0 mV :math:`V_\mathrm{reset}` Reset potential of the membrane
``C_m`` 250.0 pF :math:`C_\mathrm{m}` Capacitance of the membrane
``g_L`` 25.0 nS :math:`g_\mathrm{L}` Leak conductance
``t_ref`` 2.0 ms :math:`t_\mathrm{ref}` Duration of refractory period
``C_m`` 250.0 pF :math:`C_\mathrm{m}` Capacitance of the membrane
``g_L`` 25.0 nS :math:`g_\mathrm{L}` Leak conductance
``t_ref`` 2.0 ms :math:`t_\mathrm{ref}` Duration of refractory period
``tau_AMPA`` 2.0 ms :math:`\tau_\mathrm{AMPA}` Time constant of AMPA synapse
``tau_GABA`` 5.0 ms :math:`\tau_\mathrm{GABA}` Time constant of GABA synapse
``tau_rise_NMDA`` 2.0 ms :math:`\tau_\mathrm{NMDA,rise}` Rise time constant of NMDA synapse
``tau_rise_NMDA`` 2.0 ms :math:`\tau_\mathrm{NMDA,rise}` Rise time constant of NMDA synapse
``tau_decay_NMDA`` 100.0 ms :math:`\tau_\mathrm{NMDA,decay}` Decay time constant of NMDA synapse
``alpha`` 0.5 ms^{-1} :math:`\alpha` Rise-time coupling strength for NMDA synapse
``conc_Mg2`` 1.0 mM :math:`[\mathrm{Mg}^+]` Extracellular magnesium concentration
Expand All @@ -137,9 +139,9 @@ The following state variables evolve during simulation and are available either
**State variable** **Initial value** **Math equivalent** **Description**
================== ================= ========================== =================================
``V_m`` -70 mV :math:`V_{\mathrm{m}}` Membrane potential
``s_AMPA`` 0 :math:`s_\mathrm{AMPA}` AMPA gating variable
``s_GABA`` 0 :math:`s_\mathrm{GABA}` GABA gating variable
``s_NMDA`` 0 :math:`s_\mathrm{NMDA}` NMDA gating variable
``s_AMPA`` 0 :math:`s_\mathrm{AMPA}` AMPA gating variable
``s_GABA`` 0 :math:`s_\mathrm{GABA}` GABA gating variable
``s_NMDA`` 0 :math:`s_\mathrm{NMDA}` NMDA gating variable
``I_NMDA`` 0 pA :math:`I_\mathrm{NMDA}` NMDA current
``I_AMPA`` 0 pA :math:`I_\mathrm{AMPA}` AMPA current
``I_GABA`` 0 pA :math:`I_\mathrm{GABA}` GABA current
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3 changes: 3 additions & 0 deletions models/iaf_psc_exp.h
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Expand Up @@ -135,6 +135,9 @@ on the synaptic time constant according to
will numerically behave as if ``tau_m`` is equal to ``tau_syn_ex`` or
``tau_syn_in``, respectively, to avoid numerical instabilities.
NEST uses exact integration [2]_, [3]_ to integrate subthreshold membrane dynamics
with maximum precision.
For implementation details see the
`IAF Integration Singularity notebook <../model_details/IAF_Integration_Singularity.ipynb>`_.
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4 changes: 2 additions & 2 deletions pynest/examples/wang_decision_making.py
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Expand Up @@ -36,8 +36,8 @@
References
~~~~~~~~~~
.. [1] Wang X-J (2002). Probabilistic Decision Making by Slow Reverberation in
Cortical Circuits. Neuron, Volume 36, Issue 5, Pages 955-968.
https://doi.org/10.1016/S0896-6273(02)01092-9.
Cortical Circuits. Neuron, Volume 36, Issue 5, Pages 955-968.
https://doi.org/10.1016/S0896-6273(02)01092-9.
"""

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