From 64e87af964e410af5bed6d482ada0f74e3f8751f Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 28 Nov 2023 09:39:13 +0100 Subject: [PATCH 1/2] fix broken links --- models/iaf_tum_2000.h | 4 ++-- .../examples/iaf_tum_2000_short_term_depression.py | 10 +++++----- .../examples/iaf_tum_2000_short_term_facilitation.py | 12 ++++++------ 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/models/iaf_tum_2000.h b/models/iaf_tum_2000.h index 6083791f32..d26dbb12c7 100644 --- a/models/iaf_tum_2000.h +++ b/models/iaf_tum_2000.h @@ -51,7 +51,7 @@ plasticity and exponential shaped postsynaptic currents (PSCs). In particular, ``iaf_tum_2000`` implements short-term depression and short-term facilitation according to [1]_ by solving Eqs. (3) and (4) from that paper in an exact manner. -``iaf_tum_2000`` differs from `iaf_psc_exp <../models/iaf_psc_exp>` by the addition +``iaf_tum_2000`` differs from :doc:`iaf_psc_exp <../models/iaf_psc_exp>` by the addition of synaptic state variables :math:`x`, :math:`z` and :math:`u`, which together with the membrane potential :math:`V_\text{m}` and synaptic current :math:`I_\text{syn}` obey the following dynamics: @@ -77,7 +77,7 @@ where :math:`\Gamma_X` is an index set over either excitatory (:math:`\text{X} = :math:`k` indexes the spike times of neuron :math:`j`, and :math:`d_j` is the delay from neuron :math:`j`. -``iaf_tum_2000`` incorporates the `tsodyks_synapse <../models/tsodyks_synapse>` +``iaf_tum_2000`` incorporates the :doc:`tsodyks_synapse ` computations directly in the presynaptic neuron, that is, the synaptic state variables :math:`x,y,z,u` are integrated in the presynaptic neuron instead of the synapse model. For a presynaptic neuron with :math:`K` outgoing connections diff --git a/pynest/examples/iaf_tum_2000_short_term_depression.py b/pynest/examples/iaf_tum_2000_short_term_depression.py index e325289860..ac6ac2cc78 100644 --- a/pynest/examples/iaf_tum_2000_short_term_depression.py +++ b/pynest/examples/iaf_tum_2000_short_term_depression.py @@ -23,7 +23,7 @@ Short-term depression example ----------------------------- -The `iaf_tum_2000 <../models/iaf_tum_2000>` neuron [1]_ is a model with +The :doc:`iaf_tum_2000 ` neuron [1]_ is a model with *short-term synaptic plasticity*. Short-term plasticity can either strengthen or weaken a synapse and acts on a timescale of milliseconds to seconds. This example illustrates *short-term depression*, which is caused by depletion of @@ -48,11 +48,11 @@ .. note:: - The `iaf_tum_2000 <../models/iaf_tum_2000>` neuron model combined with - `static_synapse <../models/static_synapse>` provides a more efficient + The :doc:`iaf_tum_2000 ` neuron model combined with + :doc:`static_synapse ` provides a more efficient implementation of the model studied in [1]_ and [2]_ than the combination - of `iaf_psc_exp <../models/iaf_psc_exp>` with - `tsodyks_synapse <../models/tsodyks_synapse>`. + of :doc:`iaf_psc_exp ` with + :doc:`tsodyks_synapse `. References ~~~~~~~~~~ diff --git a/pynest/examples/iaf_tum_2000_short_term_facilitation.py b/pynest/examples/iaf_tum_2000_short_term_facilitation.py index 57d321b4a2..5045ac03e0 100644 --- a/pynest/examples/iaf_tum_2000_short_term_facilitation.py +++ b/pynest/examples/iaf_tum_2000_short_term_facilitation.py @@ -23,7 +23,7 @@ Short-term facilitation example ------------------------------- -The `iaf_tum_2000 <../models/iaf_tum_2000>` neuron [1]_ is a model with +The :doc:`iaf_tum_2000 ` neuron [1]_ is a model with *short-term synaptic plasticity*. Short-term plasticity can either strengthen or weaken a synapse and acts on a timescale of milliseconds to seconds. This example illustrates *short-term facilitation*, which is a transient increase @@ -50,11 +50,11 @@ .. note:: - The `iaf_tum_2000 <../models/iaf_tum_2000>` neuron model combined with - `static_synapse <../models/static_synapse>` provides a more efficient + The :doc:`iaf_tum_2000 ` neuron model combined with + :doc:`static_synapse ` provides a more efficient implementation of the model studied in [1]_ and [2]_ than the combination - of `iaf_psc_exp <../models/iaf_psc_exp>` with - `tsodyks_synapse <../models/tsodyks_synapse>`. + of :doc:`iaf_psc_exp ` with + :doc:`tsodyks_synapse `. References ~~~~~~~~~~ @@ -69,7 +69,7 @@ See Also ~~~~~~~~ -:doc:`../models/iaf_tum_2000`, :doc:`iaf_tum_2000_short_term_depression` +:doc:`/models/iaf_tum_2000`, :doc:`iaf_tum_2000_short_term_depression` """ import matplotlib.pyplot as plt From b7dcfc49935424e13b4f7bcbd78e42d5fac157e0 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 28 Nov 2023 09:40:49 +0100 Subject: [PATCH 2/2] make abs path --- models/iaf_tum_2000.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/iaf_tum_2000.h b/models/iaf_tum_2000.h index d26dbb12c7..e81e14dbd9 100644 --- a/models/iaf_tum_2000.h +++ b/models/iaf_tum_2000.h @@ -51,7 +51,7 @@ plasticity and exponential shaped postsynaptic currents (PSCs). In particular, ``iaf_tum_2000`` implements short-term depression and short-term facilitation according to [1]_ by solving Eqs. (3) and (4) from that paper in an exact manner. -``iaf_tum_2000`` differs from :doc:`iaf_psc_exp <../models/iaf_psc_exp>` by the addition +``iaf_tum_2000`` differs from :doc:`iaf_psc_exp ` by the addition of synaptic state variables :math:`x`, :math:`z` and :math:`u`, which together with the membrane potential :math:`V_\text{m}` and synaptic current :math:`I_\text{syn}` obey the following dynamics: