Merge pull request #220 from Hananel-Hazan/hananel

Adding updated benchmark

README.md

@@ -99,6 +99,17 @@ 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).
 
+## Benchmarking
+We simulated a network with a population of n Poisson input neurons with firing rates (in Hertz) drawn randomly from U(0, 100), connected all-to-all with a equally-sized population of leaky integrate-and-fire (LIF) neurons, with connection weights sampled from N(0,1). We varied n systematically from 250 to 10,000 in steps of 250, and ran each simulation with every library for 1,000ms with a time resolution dt = 1.0. We tested BindsNET (with CPU and GPU computation), BRIAN2, PyNEST (the Python interface to the NEST SLI interface that runs the C++NEST core simulator), ANNarchy (with CPU and GPU computation), and BRIAN2genn (the BRIAN2 front-end to the GeNN simulator).
+
+Several packages, including BRIAN and PyNEST, allow the setting of certain global preferences; e.g., the number of CPU threads, the number of OpenMP processes, etc. We chose these settings for our benchmark study in an attempt to maximize each library's speed, but note that BindsNET requires no setting of such options. Our approach, inheriting the computational model of PyTorch, appears to make the best use of the available hardware, and therefore makes it simple for practicioners to get the best performance from their system with the least effort.
+
+<p align="middle">
+<img src="https://github.com/Hananel-Hazan/bindsnet/blob/hananel/docs/BindsNET%20benchmark.png" alt="BindsNET%20Benchmark"  width="503" height="403">
+</p>
+
+All simulations run on Ubuntu 16.04 LTS with Intel(R) Xeon(R) CPU E5-2687W v3 @ 3.10GHz, 128Gb RAM @ 2133MHz, and two GeForce GTX TITAN X (GM200) GPUs. Python 3.6 is used in all cases. Clock time was recorded for each simulation run. 
+
 ## Citation
 
 If you use BindsNET in your research, please cite the following [article](https://www.frontiersin.org/article/10.3389/fninf.2018.00089):