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jessica-mitchell merged 2 commits into from
Nov 28, 2023
Merged

Fix broken links in documentation #3013

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64e87af
fix broken links
jessica-mitchell Nov 28, 2023
b7dcfc4
make abs path
jessica-mitchell Nov 28, 2023
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4 changes: 2 additions & 2 deletions models/iaf_tum_2000.h
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Expand Up @@ -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:
Expand All @@ -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 </models/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
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10 changes: 5 additions & 5 deletions pynest/examples/iaf_tum_2000_short_term_depression.py
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Expand Up @@ -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 </models/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
Expand All @@ -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 </models/iaf_tum_2000>` neuron model combined with
:doc:`static_synapse </models/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 </models/iaf_psc_exp>` with
:doc:`tsodyks_synapse </models/tsodyks_synapse>`.

References
~~~~~~~~~~
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12 changes: 6 additions & 6 deletions pynest/examples/iaf_tum_2000_short_term_facilitation.py
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Expand Up @@ -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 </models/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
Expand All @@ -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 </models/iaf_tum_2000>` neuron model combined with
:doc:`static_synapse </models/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 </models/iaf_psc_exp>` with
:doc:`tsodyks_synapse </models/tsodyks_synapse>`.

References
~~~~~~~~~~
Expand All @@ -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
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