typo

neural-networks-1.md

@@ -61,7 +61,7 @@ class Neuron(object):
 In other words, each neuron performs a dot product with the input and its weights, adds the bias and applies the non-linearity (or activation function), in this case the sigmoid \\(\sigma(x) = 1/(1+e^{-x})\\). We will go into more details about different activation functions at the end of this section.
 
 
-**Coarse model.** It's important to stress that this model of a biological neuron is very coarse: For example, there are many different types of neurons, each with different properties. The dendrites in biological neurons perform complex nonlinear computations. The synapses are not just a single weight, they're a complex non-linear dynamical system. The exact timing of the output spikes in many systems in known to be important, suggesting that the rate code approximation may not hold. Due to all these and many other simplifications, be prepared to hear groaning sounds from anyone with some neuroscience background if you draw analogies between Neural Networks and real brains. See this [review](https://physics.ucsd.edu/neurophysics/courses/physics_171/annurev.neuro.28.061604.135703.pdf) (pdf), or more recently this [review](http://www.sciencedirect.com/science/article/pii/S0959438814000130) if you are interested.
+**Coarse model.** It's important to stress that this model of a biological neuron is very coarse: For example, there are many different types of neurons, each with different properties. The dendrites in biological neurons perform complex nonlinear computations. The synapses are not just a single weight, they're a complex non-linear dynamical system. The exact timing of the output spikes in many systems is known to be important, suggesting that the rate code approximation may not hold. Due to all these and many other simplifications, be prepared to hear groaning sounds from anyone with some neuroscience background if you draw analogies between Neural Networks and real brains. See this [review](https://physics.ucsd.edu/neurophysics/courses/physics_171/annurev.neuro.28.061604.135703.pdf) (pdf), or more recently this [review](http://www.sciencedirect.com/science/article/pii/S0959438814000130) if you are interested.
 
 <a name='classifier'></a>