Updated documentation.

Author: CodeReclaimers

docs/ctrnn.rst

@@ -0,0 +1,22 @@
+Continuous-time recurrent neural network implementation
+=======================================================
+
+The default continuous-time recurrent neural network (CTRNN) implementation in neat-python
+is modeled as a system of ordinary differential equations, with neuron potentials as the
+dependent variables.
+
+:math:`\tau_i \frac{d y_i}{dt} = -y_i + f_i\left(\beta_i + \sum\limits_{j \in A_i} w_{ij} y_j\right)`
+
+
+Where:
+
+* :math:`\tau_i` is the time constant of neuron :math:`i`.
+* :math:`y_i` is the potential of neuron :math:`i`.
+* :math:`f_i` is the activation function of neuron :math:`i`.
+* :math:`\beta_i` is the bias of neuron :math:`i`.
+* :math:`A_i` is the set of indices of neurons that provide input to neuron :math:`i`.
+* :math:`w_{ij}` is the weight of the connection from neuron :math:`j` to neuron :math:`i`.
+
+The time evolution of the network is computed using the forward Euler method:
+
+:math:`y_i(t+\Delta t) = y_i(t) + \Delta t \frac{d y_i}{dt}`

docs/index.rst

@@ -4,6 +4,9 @@ Welcome to NEAT-Python's documentation!
 NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural
 networks. NEAT-Python is a pure Python implementation of NEAT, with no dependencies other than the Python standard library.
 
+*Note*: Some of the sample code has other dependencies; please see each sample's README file for additional details
+and installation/setup instructions.
+
 Support for HyperNEAT and other extensions to NEAT is planned once the fundamental NEAT implementation is
 more complete and stable.
 
@@ -26,8 +29,7 @@ Contents:
    xor_example
    customization
    activation
-
-
+   ctrnn
 
 Indices and tables
 ==================