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brainpy · Oct 27, 2021 · c7135eb · c7135eb
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diff --git a/cann/Wu_2008_CANN.ipynb b/cann/Wu_2008_CANN.ipynb
@@ -28,7 +28,7 @@
    "id": "aa926f46",
    "metadata": {},
    "source": [
-    "The mathematical equation of the Continuous-attractor Neural Network (cann) is given by:\n",
+    "The mathematical equation of the Continuous-Attractor Neural Network (CANN) is given by:\n",
     "\n",
     "$$\\tau \\frac{du(x,t)}{dt} = -u(x,t) + \\rho \\int dx' J(x,x') r(x',t)+I_{ext}$$\n",
     "\n",
@@ -180,10 +180,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b463a007",
+   "id": "aa8cb3c4",
    "metadata": {},
    "source": [
-    "<img src=\"../images/cann-encoding.gif\">"
+    "![](../images/cann-encoding.gif)"
    ]
   },
   {
@@ -264,10 +264,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db638c78",
+   "id": "deb96b0b",
    "metadata": {},
    "source": [
-    "<img src=\"../images/cann-decoding.gif\">"
+    "![](../images/cann-decoding.gif)"
    ]
   },
   {
@@ -343,10 +343,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20fbc890",
+   "id": "dc4c39bf",
    "metadata": {},
    "source": [
-    "<img src=\"../images/cann-tracking.gif\">"
+    "![](../images/cann-tracking.gif)"
    ]
   }
  ],

diff --git a/cann/Wu_2008_CANN.py b/cann/Wu_2008_CANN.py
@@ -27,7 +27,7 @@
 # - Chaoming Wang ([email protected])
 
 # %% [markdown]
-# The mathematical equation of the Continuous-attractor Neural Network (cann) is given by:
+# The mathematical equation of the Continuous-Attractor Neural Network (CANN) is given by:
 #
 # $$\tau \frac{du(x,t)}{dt} = -u(x,t) + \rho \int dx' J(x,x') r(x',t)+I_{ext}$$
 #
@@ -122,7 +122,7 @@ def update(self, _t, _dt):
 )
 
 # %% [markdown]
-# <img src="../images/cann-encoding.gif">
+# ![](../images/cann-encoding.gif)
 
 # %% [markdown]
 # ## Template matching
@@ -153,7 +153,7 @@ def update(self, _t, _dt):
 )
 
 # %% [markdown]
-# <img src="../images/cann-decoding.gif">
+# ![](../images/cann-decoding.gif)
 
 # %% [markdown]
 # ## Smooth tracking
@@ -184,4 +184,4 @@ def update(self, _t, _dt):
 )
 
 # %% [markdown]
-# <img src="../images/cann-tracking.gif">
+# ![](../images/cann-tracking.gif)
diff --git a/images/izhikevich.jfif b/images/izhikevich.jfif
diff --git a/neurons/Izhikevich_2003_Izhikevich_model.ipynb b/neurons/Izhikevich_2003_Izhikevich_model.ipynb
@@ -21,13 +21,6 @@
     "Izhikevich neuron model reproduces spiking and bursting behavior of known types of cortical neurons. This model combines the biologically plausibility of Hodgkin–Huxley-type dynamics (HH model) and the computational efficiency of integrate-and-fire neurons (LIF model) ."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![](../images/izhikevich.jfif)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/neurons/Izhikevich_2003_Izhikevich_model.py b/neurons/Izhikevich_2003_Izhikevich_model.py
@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.4
+#       jupytext_version: 1.11.5
 #   kernelspec:
 #     display_name: brainpy
 #     language: python
@@ -23,9 +23,6 @@
 # %% [markdown]
 # Izhikevich neuron model reproduces spiking and bursting behavior of known types of cortical neurons. This model combines the biologically plausibility of Hodgkin–Huxley-type dynamics (HH model) and the computational efficiency of integrate-and-fire neurons (LIF model) .
 
-# %% [markdown]
-# ![](../images/izhikevich.jfif)
-
 # %% [markdown]
 # Bifurcation methodologies enable us to reduce many biophysically accurate Hodgkin–Huxley-type neuronal models to a two-dimensional system of ordinary differential equations of the form：
 #