Merge pull request #3275 from jessica-mitchell/minor-doc-fixes

Fix minor issues in docs

Author: jessica-mitchell

doc/htmldoc/neurons/exact-integration.rst

@@ -22,9 +22,9 @@ The leaky integrate-and fire model
 In the leaky integrate-and-fire model, the memory problem is solved by adding a "leak" term :math:`\frac{-1}{R}V` (:math:`R` is the resistance and :math:`\tau=RC`) to the membrane potential:
 
 .. math::
+    :label: membrane
 
     \frac{dV}{dt}=\frac{-1}{\tau}V+\frac{1}{C}I.
-    :label: membrane
 
 This reflects the diffusion of ions that occurs through the membrane when some equilibrium is not reached in the cell.
 

doc/htmldoc/synapses/connectivity_concepts.rst

@@ -69,6 +69,7 @@ Projections are created in NEST with the :py:func:`.Connect` function:
     nest.Connect(pre, post)
     nest.Connect(pre, post, conn_spec)
     nest.Connect(pre, post, conn_spec, syn_spec)
+    nest.Connect(pre, post, conn_spec, syn_spec, return_synapsecollection=True)
 
 In the simplest case, the function just takes the ``NodeCollections`` ``pre`` and ``post``, defining the nodes of
 origin (`sources`) and termination (`targets`) for the connections to be established with the default rule ``all-to-all`` and the synapse model :ref:`static_synapse`.

doc/htmldoc/synapses/index.rst

@@ -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`

models/iaf_bw_2001.h

@@ -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`.
@@ -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>`_
@@ -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
@@ -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
@@ -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
 

models/iaf_bw_2001_exact.h

@@ -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)
@@ -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
 ++++++++++
 
@@ -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
@@ -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

models/iaf_psc_exp.h

@@ -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>`_.
 

pynest/examples/one_neuron_with_noise.py

@@ -56,7 +56,7 @@
 ###############################################################################
 # Third, the Poisson generator is configured using ``SetStatus``, which expects
 # a list of node handles and a list of parameter dictionaries. We set the
-# Poisson generators to 8,000 Hz and 15,000 Hz, respectively. Note that we do
+# Poisson generators to 80,000 Hz and 15,000 Hz, respectively. Note that we do
 # not need to set parameters for the neuron and the voltmeter, since they have
 # satisfactory defaults.
 

pynest/examples/wang_decision_making.py

@@ -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.
 
 """