Merge pull request #74 from Hananel-Hazan/dan

Cleaning up Nodes code, adding current-based LIF, lots of refactoring.

README.md

@@ -4,7 +4,7 @@ A Python package used for simulating spiking neural networks (SNNs) on CPUs or G
 
 BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning.
 
-This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the [Biologically Inspired Neural & Dynamical Systems (BINDS) lab](http://binds.cs.umass.edu/). 
+This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the [Biologically Inspired Neural & Dynamical Systems (BINDS) lab](http://binds.cs.umass.edu/).
 
 [![Build Status](https://travis-ci.com/Hananel-Hazan/bindsnet.svg?token=trym5Uzx1rs9Ez2yENEF&branch=master)](https://travis-ci.com/Hananel-Hazan/bindsnet)
 [![Documentation Status](https://readthedocs.org/projects/bindsnet-docs/badge/?version=latest)](https://bindsnet-docs.readthedocs.io/?badge=latest)
@@ -78,8 +78,25 @@ We are interested in applying SNNs to ML and RL problems. We use STDP to modify
 
 We have provided some simple starter scripts for doing unsupervised learning (learning a fully-connected or convolutional representation via STDP), supervised learning (clamping output neurons to desired spiking behavior depending on data labels), and reinforcement learning (converting observations from the Atari game Space Invaders to input to an SNN, and converting network activity back to actions in the game).
 
-## References
- Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma, [BindsNET: A machine learning-oriented spiking neural networks library in Python.](https://arxiv.org/abs/1806.01423) 2018, Arxiv.
+## Citation
+
+If you use BindsNET in your research, please cite the following [article](https://arxiv.org/abs/1806.01423):
+
+```
+@ARTICLE{2018arXiv180601423H,
+   author = {{Hazan}, H. and {Saunders}, D.~J. and {Khan}, H. and {Sanghavi}, D.~T. and 
+	{Siegelmann}, H.~T. and {Kozma}, R.},
+    title = "{BindsNET: A machine learning-oriented spiking neural networks library in Python}",
+  journal = {ArXiv e-prints},
+archivePrefix = "arXiv",
+   eprint = {1806.01423},
+ keywords = {Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition},
+     year = 2018,
+    month = jun,
+   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180601423H},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+```
 
 ## Contributors
 

bindsnet/analysis/visualization.py

@@ -14,10 +14,9 @@ def plot_weights_movie(ws, sample_every=1):
     
     Inputs:
     
-        | :code:`ws` (:code:`numpy.array`): Numpy array of shape :code:`[N_examples, source, target, time]`
-        | :code:`sample_every` (:code:`int`): Sub-sample using this parameter. For example if :code:`time` is
-                                                                  too large (500), set this parameter to 20 to sample weights 
-                                                                  every 20 iterations. 
+        | :code:`ws` (:code:`numpy.array`): Numpy array
+        of shape :code:`[n_examples, source, target, time]`
+        | :code:`sample_every` (:code:`int`): Sub-sample using this parameter. 
     """
     weights = []
 
@@ -49,10 +48,13 @@ def plot_spike_trains_for_example(spikes, n_ex=None, top_k=None, indices=None):
     
     Inputs:
         
-        | :code:`spikes` (:code:`torch.Tensor (n_examples, n_neurons, time)`): Spiking train data for a population of neurons for one example.
-        | :code:`n_ex` (:code:`int`): Allows user to pick which example to plot spikes for. Must be >= 0.
+        | :code:`spikes` (:code:`torch.Tensor (n_examples, n_neurons, time)`):
+        Spiking train data for a population of neurons for one example.
+        | :code:`n_ex` (:code:`int`): Allows user to pick
+        which example to plot spikes for. Must be >= 0.
         | :code:`top_k` (:code:`int`): Plot k neurons that spiked the most for n_ex example.
-        | :code:`indices` (:code:`list(int)`): Plot specific neurons' spiking activity instead of top_k. Meant to replace top_k. 
+        | :code:`indices` (:code:`list(int)`): Plot specific neurons'
+        spiking activity instead of top_k. Meant to replace top_k. 
     '''
 
     assert (n_ex is not None and n_ex >= 0 and n_ex < spikes.shape[0])
@@ -84,11 +86,16 @@ def plot_voltage(voltage, n_ex=0, n_neuron=0, time=None, threshold=None):
     
     Inputs:
         
-        | :code:`voltage` (:code:`torch.Tensor` or :code:`numpy.array`): Tensor or array of shape :code:`[n_examples, n_neurons, time]`.
-        | :code:`n_ex` (:code:`int`): Allows user to pick which example to plot voltage for.
-        | :code:`n_neuron` (:code:`int`): Neuron index for which to plot voltages for.
-        | :code:`time` (:code:`tuple(int)`): Plot spiking activity of neurons between the given range of time. 
-        | :code:`threshold` (:code:`float`): Neuron spiking threshold. Will be shown on the plot.
+        | :code:`voltage` (:code:`torch.Tensor` or :code:`numpy.array`):
+        Tensor or array of shape :code:`[n_examples, n_neurons, time]`.
+        | :code:`n_ex` (:code:`int`): Allows user
+        to pick which example to plot voltage for.
+        | :code:`n_neuron` (:code:`int`): Neuron
+        index for which to plot voltages for.
+        | :code:`time` (:code:`tuple(int)`): Plot spiking
+        activity of neurons between the given range of time. 
+        | :code:`threshold` (:code:`float`): Neuron
+        spiking threshold. Will be shown on the plot.
     '''
 
     assert (n_ex >= 0 and n_neuron >= 0)
@@ -105,7 +112,9 @@ def plot_voltage(voltage, n_ex=0, n_neuron=0, time=None, threshold=None):
 
     plt.figure()
     plt.plot(voltage[n_ex, n_neuron, timer])
-    plt.xlabel('Simulation Time'); plt.ylabel('Voltage'); plt.title('Membrane voltage of neuron %d for example %d'%(n_neuron, n_ex+1))
+    plt.xlabel('Simulation Time')
+    plt.ylabel('Voltage')
+    plt.title('Membrane voltage of neuron %d for example %d' % (n_neuron, n_ex + 1))
     locs, labels = plt.xticks()
     locs = range(int(locs[1]), int(locs[-1]), 10)
     plt.xticks(locs, time_ticks)