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diff --git a/demos/animation_demo/README.md b/demos/animation_demo/README.md
@@ -1,4 +1,4 @@
-### Animating Network Automata
+# Animating Network Automata
 
 Network Automata can be animated with the Netomaton `animate` function.
 

diff --git a/demos/asynchronous_automata/README.md b/demos/asynchronous_automata/README.md
@@ -1,4 +1,4 @@
-### Asynchronous Automata
+# Asynchronous Automata
 
 Network Automata are usually processed synchronously. That is, each node's
 state is based strictly on the activity of its neighbourhood in the previous

diff --git a/demos/ca_density_classification/README.md b/demos/ca_density_classification/README.md
@@ -1,4 +1,4 @@
-### Density Classification with Evolved 1D Cellular Automata
+# Density Classification with Evolved 1D Cellular Automata
 
 When creating a 1D Cellular Automaton adjacency matrix, the size of the
 cell neighbourhood can be varied by setting the parameter _*r*_. The

diff --git a/demos/continuous_automata/README.md b/demos/continuous_automata/README.md
@@ -1,4 +1,4 @@
-### Continuous Automata
+# Continuous Automata
 
 Network Automata needn't consist of discrete activities. The units in an
 automaton can also take on continuous-valued activities.

diff --git a/demos/continuous_time_models/README.md b/demos/continuous_time_models/README.md
@@ -1,4 +1,4 @@
-### Continuous-Time Models
+# Continuous-Time Models
 
 Some models of computation treat time as continuous. These kinds of
 models often define "equations of motion" that describe the dynamics

diff --git a/demos/elementary_ca/README.md b/demos/elementary_ca/README.md
@@ -1,4 +1,4 @@
-### Elementary Cellular Automata
+# Elementary Cellular Automata
 
 This example demonstrates the Rule 30 Elementary Cellular Automaton. Currently, only 1- and 2-dimensional _k_-color
 Cellular Automata with periodic boundary conditions are supported. The size of the neighbourhood can be adjusted. The

diff --git a/demos/finite_state_machine/README.md b/demos/finite_state_machine/README.md
@@ -1,4 +1,4 @@
-### Finite State Machine
+# Finite State Machine
 
 This example illustrates the construction of a Finite State Machine.
 Although simplistic, it demonstrates how Finite State Machines can be

diff --git a/demos/game_of_life/README.md b/demos/game_of_life/README.md
@@ -1,4 +1,4 @@
-### Conway's Game of Life
+# Conway's Game of Life
 
 Conway's Game of Life is a well-known 2D Cellular Automaton.
 

diff --git a/demos/hexagonal_ca/README.md b/demos/hexagonal_ca/README.md
@@ -1,4 +1,4 @@
-### Hexagonal Cell Lattices
+# Hexagonal Cell Lattices
 
 Netomaton supports automata with hexagonal node lattices. A hexagonal
 node with a neighbourhood of radius 1 is depicted below:

diff --git a/demos/hopfield_net/README.md b/demos/hopfield_net/README.md
@@ -1,4 +1,4 @@
-### Hopfield Network
+# Hopfield Network
 
 The Hopfield Network can be thought of as a network automaton with a
 complete graph. The nodes (or neurons) are binary units, and

diff --git a/demos/hopfield_tank_tsp/README.md b/demos/hopfield_tank_tsp/README.md
@@ -1,4 +1,4 @@
-### Travelling Salesman Problem with the Hopfield-Tank Neural Net
+# Travelling Salesman Problem with the Hopfield-Tank Neural Net
 
 The Hopfield-Tank Neural Network is a model of a network of densely
 connected non-linear analog neurons. The model provides solutions to the

diff --git a/demos/langtons_lambda/README.md b/demos/langtons_lambda/README.md
@@ -1,4 +1,4 @@
-### Langton's Lambda and Measures of Complexity
+# Langton's Lambda and Measures of Complexity
 
 One way to specify Cellular Automata rules is with rule tables. Rule tables are enumerations of all possible
 neighbourhood states together with their node state mappings. For any given neighbourhood state, a rule table provides

diff --git a/demos/perturbation_demo/README.md b/demos/perturbation_demo/README.md
@@ -1,4 +1,4 @@
-### Perturbations
+# Perturbations
 
 Network Automata may be perturbed at any point in time during their
 evolution. This may model the effects of some external forcing applied

diff --git a/demos/pushdown_automata/README.md b/demos/pushdown_automata/README.md
@@ -1,4 +1,4 @@
-### Pushdown Automata
+# Pushdown Automata
 
 This example illustrates the construction of a Pushdown Automaton.
 Although simplistic, it demonstrates how Pushdown Automata can be

diff --git a/demos/reaction_diffusion/README.md b/demos/reaction_diffusion/README.md
@@ -1,4 +1,4 @@
-### Gray-Scott Reaction-Diffusion Model
+# Gray-Scott Reaction-Diffusion Model
 
 This example attempts to demonstrate the Gray-Scott Reaction-Diffusion
 model. The reactions are:

diff --git a/demos/reversible_ca/README.md b/demos/reversible_ca/README.md
@@ -1,4 +1,4 @@
-### Reversible 1D Cellular Automata
+# Reversible 1D Cellular Automata
 
 Network automata can be explicitly made to be reversible. The following example demonstrates the
 creation of the elementary reversible Cellular Automaton rule 90R:

diff --git a/demos/sandpiles/README.md b/demos/sandpiles/README.md
@@ -1,4 +1,4 @@
-### Sandpiles
+# Sandpiles
 
 Netomaton offers an implementation of the Abelian sandpile model.
 

diff --git a/demos/small_world_density_classification/README.md b/demos/small_world_density_classification/README.md
@@ -1,4 +1,4 @@
-### Density classification with a small-world network
+# Density classification with a small-world network
 
 This demo is inspired by "Collective dynamics of ‘small-world’ networks",
 by Duncan J. Watts and Steven H. Strogatz (Nature 393, no. 6684

diff --git a/demos/substitution_systems/README.md b/demos/substitution_systems/README.md
@@ -1 +1 @@
-### Substitution Systems
+# Substitution Systems
diff --git a/demos/timesteps_and_input/README.md b/demos/timesteps_and_input/README.md
@@ -1,4 +1,4 @@
-### Timesteps and Input
+# Timesteps and Input
 
 The Netomaton `evolve` function accepts two different parameters that
 are used to specify how the network should be evolved: the `timesteps`

diff --git a/demos/totalistic_ca/README.md b/demos/totalistic_ca/README.md
@@ -1,4 +1,4 @@
-### 1D Cellular Automata with Totalistic Rules
+# 1D Cellular Automata with Totalistic Rules
 
 The number of states, or colors, that a cell in a Cellular Automaton can adopt is given by _k_. For example, in a binary Cellular Automaton a cell can
 assume only values of 0 and 1, and thus has _k_ = 2. A built-in function, `totalistic_ca`,

diff --git a/demos/totalistic_ca2d/README.md b/demos/totalistic_ca2d/README.md
@@ -1,4 +1,4 @@
-### 2D Cellular Automata
+# 2D Cellular Automata
 
 Netomaton supports 2-dimensional Cellular Automata with periodic
 boundary conditions. The number of states, k, can be any whole number.

diff --git a/demos/turing_machine/README.md b/demos/turing_machine/README.md
@@ -1,4 +1,4 @@
-### Turing Machine
+# Turing Machine
 
 There are two different ways that a Turing Machine can be described in
 the context of the Netomaton framework: