Update readme.

README.md

@@ -19,12 +19,15 @@ networks with PyTorch-like syntax,
 
 JPC provides a **simple**, **relatively fast** and **flexible** API for 
 training of a variety of PCNs including discriminative, generative and hybrid 
-models. Like JAX, JPC is completely functional, and the core library is <1000 
-lines of code. Unlike existing implementations, JPC leverages ordinary 
-differential equation (ODE) solvers to integrate the inference dynamics of PC 
-networks (PCNs), which we find can provide significant speed-ups compared to 
-standard optimisers, especially for deeper models. JPC also provides some 
-analytical tools that can be used to study and diagnose issues with PCNs.
+models. 
+* Like JAX, JPC is completely functional, and the core library is <1000 lines 
+of code. 
+* Unlike existing implementations, JPC leverages ordinary differential 
+equation (ODE) solvers to integrate the inference dynamics of PC networks 
+(PCNs), which we find can provide significant speed-ups compared to standard 
+optimisers, especially for deeper models. 
+* JPC also provides some analytical tools that can be used to study and 
+diagnose issues with PCNs.
 
 ## Overview
 * [Installation](#installation)