Update README.md

README.md

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-# Decoding: 
+# Neural_Decoding: 
 
 ### A python package that includes many methods for decoding neural activity
 
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 This package accompanies a manuscript (soon to be released) that compares the performance of these methods on several datasets.
 
+
+## Dependencies
+In order to run all the decoders based on neural networks, you need to install [Keras] (https://keras.io/#installation) <br>
+In order to run the XGBoost Decoder, you need to install [XGBoost] (https://pypi.python.org/pypi/xgboost/) <br>
+In order to run the Wiener Filter or Wiener Cascade, you will need [scikit-learn] (http://scikit-learn.org/stable/install.html).
+
+
 ## Getting started
 We have included jupyter notebooks that provide detailed examples of how to use the decoders. The file "Examples_kf_decoder" is for the Kalman filter decoder and the file "Examples_all_decoders" is for all other decoders.
 
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  - **bin_spikes**: converts spike times to the number of spikes within time bins
  - **bin_output**: converts a continuous stream of outputs to the average output within time bins
  - **get_spikes_with_history**: using binned spikes as input, this function creates a covariate matrix of neural data that incorporates spike history
-
-## Dependencies
-In order to run all the decoders based on neural networks, you need to install [Keras] (https://keras.io/#installation) <br>
-In order to run the XGBoost Decoder, you need to install [XGBoost] (https://pypi.python.org/pypi/xgboost/) <br>
-In order to run the Wiener Filter or Wiener Cascade, you will need [scikit-learn] (http://scikit-learn.org/stable/install.html).