small tweaks

Author: neworderofjamie

content/english/neuromorphic-computing/software/snn-frameworks/genn/index.md

@@ -20,7 +20,7 @@ draft: false
 
 ## Overview
 **GeNN** is a software package to accelerate Spiking Neural Network simulations 
-on hardware including NVIDIA GPUs. GeNN uses code generation to ``computational backends'' to build simulations. The main backends are curerently C++/CUDA for NVIDIA GPUs or C++ for CPU-only mode. GenNN is available on Linux, Windows, MacOS.
+on hardware including NVIDIA GPUs. GeNN uses code generation with various 'backends' to run simulations. The main backends are currently C++/CUDA for NVIDIA GPUs or C++ for CPU-only mode. GeNN is available on Linux, Windows, MacOS.
 
 Networks are described using a simple Python API and built out of model components that can be fully customized. The behaviour of neurons, synapses, plasticity mechanisms, initialisation methods and connectivity construction are defined using Python strings containing a C-like language called GeNNCode. Users can fully customise these components. GeNN provides extensive documentation and tutorials.
 

content/english/neuromorphic-computing/software/snn-frameworks/ml_genn/index.md

@@ -23,4 +23,4 @@ draft: false
 
 mlGeNN exposes the constructs required to build SNNs using an API, inspired by modern ML libraries like Keras, which aims to reduce cognitive load by automatically calculating layer sizes, default hyperparameter values etc to enable rapid prototyping of SNN models.
 
-mlGeNN provides user friendly implementations of novel SNN training algorithms such as e-prop and EventProp to enable spike-based ML on top of GeNN’s GPU-optimised sparse data structures and algorithms. This allows better scaling and hence using EventProp at high temporal resolution with thousands of time steps. mlGeNN provides extensive documentation and tutorials.
+mlGeNN provides user friendly implementations of novel SNN training algorithms such as e-prop and EventProp to enable spike-based ML on top of GeNN’s GPU-optimised sparse data structures and algorithms. This allows better scaling and, using EventProp, allows training with high temporal resolution and/or thousands of time steps. mlGeNN provides extensive documentation and tutorials.