Difference between parameters and weights in ANN I think in writing we often refer to the part of the neural networks that tell us about the data are the parameters when we really mean the weights, is this correct? Would the sentence...


  Better understanding parameters of the neural network after training on bird migration data can allow us to comprehend the behavior of these animals.


...be better phrased as...


  Better understanding the weights of the neural network after training on bird migration data can allow us to comprehend the behavior of these animals.
That's partially right.

Apart from the weights (and bias) you also have the so called hyper-parameters. Depending on your architecture, you may have different ones. For a simple feedforward neural network, the hyperparameters are:


number of neurons
number of layers
learning rate eta (η)
regularization penalty lambda (λ)
momentum
number of epochs
batch size
dropout


etc

These hyperparameters are (usually) user-tuned. The weights (and bias) are self-trained by the network, based on optimization. Keep in mind that hyperparameters like the number of neurons or layers are immediately connected with the underlying model of your data, therefore they are an index of your dataset's complexity.