Uploaded experiment1 first version

README.md

@@ -1,2 +1,39 @@
-# spinnaker-neuromorphic-classifier
-# spinnaker-neuromorphic-classifier
+This project demonstrates multivariate classification using neuromorphic hardware, 
+in this case the SpiNNaker hardware platform [1].
+The classifier neural model design is based on that published in [2,3] which is a 
+bio-inspired design based on the insect olfactory system.
+
+The core requirements to run these programs is Python 2.7 and a SpiNNaker board with 
+the accompanying software suite made available from MAnchester Universtiy;
+The work is demonstrated here in a series of Python/PynNN/sPyNNaker experiments of 
+increasing complexity/difficulty.
+In all these experiments, for those unable to run the program themselves, screenshot(s) of the 
+relevant raster plots are made available in the "screenshots" subdirectory.
+
+EXPERIMENT 1
+This experiment demonstrates a basic ability to use synapse plasticity to learn to 
+classify ("recognise") 10 digits taken from the standard MNIST dataset.
+
+ACKNOWLEDGEMENTS
+This work is supported by a grant from the Human Brain Project (HBP).
+The SpiNNaker hardware platform is developed at Manchester University who supplied the board and software suite used in this work. 
+
+CITING THIS WORK
+To cite the work in this project please contact one of the Github collaborators based at the University of Sussex.
+
+REFERENCES
+[1] M. M. Khan, D. R. Lester, L. A. Plana, A. Rast, X. Jin, E. Painkras, and
+S. B. Furber, “SpiNNaker: Mapping neural networks onto a massively-
+parallel chip multiprocessor,” in Proceedings of the International Joint
+Conference on Neural Networks, 2008, pp. 2849–2856
+
+[2] M. Schmuker, T. Pfeil, and M. Nawrot, “A neuromorphic network for
+generic multivariate data classification,” Proc. Natl. Acad. Sci., pp. 1–6,
+Jan. 2014.
+
+[3] A. Diamond, M. Schmuker, A.Z. Berna, S.Trowell and T. Nowotny
+"Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks 
+based on the insect olfactory system", BMC Neuroscience 2014, 15(Suppl 1):P77  
+doi:10.1186/1471-2202-15-S1-P77
+
+

README.md~

@@ -0,0 +1,26 @@
+This project demonstrates multivariate classification using neuromorphic hardware, 
+in this case the SpiNNaker hardware platform [1].
+The classifier neural model design is based on that published in [2,3] which is a 
+bio-inspired design based on the insect olfactory system.
+
+The core requirements to run these programs is Python 2.7 and a SpiNNaker board with 
+the accompanying software suite made available from MAnchester Universtiy;
+The work is demonstrated here in a series of Python/PynNN/sPyNNaker experiments of 
+increasing complexity/difficulty.
+In all these experiments, for those unable to run the program themselves, screenshot(s) of the 
+relevant raster plots are made available in the "screenshots" subdirectory.
+
+EXPERIMENT 1
+This experiment demonstrates a basic ability to use synapse plasticity to learn to 
+classify ("recognise") 10 digits taken from the standard MNIST dataset.
+
+ACKNOWLEDGEMENTS
+This work is supported by a grant from the Human Brain Project (HBP).
+The SpiNNaker hardware platform is developed at Manchester University who supplied the board and software suite used in this work. 
+
+CITING THIS WORK
+To cite the work in this project please contact one of the Github collaborators based at the University of Sussex.
+
+REFERENCES
+
+

experiment1/BuildAndRunClassifier.py

@@ -0,0 +1,109 @@
+import matplotlib.pyplot as plt
+import Classifier as classifier
+import ModellingUtils as utils
+import sys
+import os.path
+import os
+
+params = eval(open("ModelParams-MNISTClassifier.txt").read())
+settings = eval(open("Settings-MNISTClassifier.txt").read())
+
+#clear marker file
+if utils.fileExists(settings['RUN_COMPLETE_FILE']):
+    os.remove(settings['RUN_COMPLETE_FILE'])
+
+#Override default params with any passed args
+numArgumentsProvided =  len(sys.argv) - 1
+
+if numArgumentsProvided >=1 :
+    settings['LEARNING'] = eval(sys.argv[1])
+if numArgumentsProvided >=2 :
+    params['NUM_VR'] = int(sys.argv[2])
+if numArgumentsProvided >=3 :
+    params['NUM_CLASSES'] = int(sys.argv[3])
+if numArgumentsProvided >=4 :
+    settings['SPIKE_SOURCE_VR_RESPONSE_PATH'] = sys.argv[4]
+if numArgumentsProvided >=5 :
+    settings['SPIKE_SOURCE_ACTIVE_CLASS_PATH'] = sys.argv[5]
+if numArgumentsProvided >=6 :
+    settings['NUM_OBSERVATIONS'] = int(sys.argv[6])
+if numArgumentsProvided >=7 :
+    settings['OBSERVATION_EXPOSURE_TIME_MS'] = int(sys.argv[7])
+if numArgumentsProvided >=8 :
+    settings['CLASS_LABELS_PATH'] = sys.argv[8]
+if numArgumentsProvided >=9 :
+    settings['CLASSIFICATION_RESULTS_PATH'] = sys.argv[8]    
+
+classifier.printParameters('Model Parameters',params)
+classifier.printParameters('Classifier Settings',settings)
+
+populationsInput = list()
+populationsNoiseSource = list()
+populationsRN = list()
+populationsPN = list()
+populationsAN = list()
+projectionsPNAN = list() #keep handle to these for saving learnt weights
+
+totalSimulationTime = settings['OBSERVATION_EXPOSURE_TIME_MS'] * settings['NUM_OBSERVATIONS']
+print 'Total Simulation Time will be', totalSimulationTime
+
+DT = 1.0 #ms Integration timestep for simulation
+
+classifier.setupModel(settings, params, DT,totalSimulationTime, populationsInput, populationsNoiseSource, populationsRN,populationsPN,populationsAN,projectionsPNAN)
+
+utils.recordPopulations(populationsInput,settings['RECORD_POP_INPUT'])
+utils.recordPopulations(populationsNoiseSource,settings['RECORD_POP_NOISE_SOURCE'])
+utils.recordPopulations(populationsRN,settings['RECORD_POP_RN'])
+utils.recordPopulations(populationsPN,settings['RECORD_POP_PN'])
+utils.recordPopulations(populationsAN,settings['RECORD_POP_AN'])
+
+#run the model for the whole learning or the whole testing period
+classifier.run(totalSimulationTime)
+
+if settings['DISPLAY_RASTER_PLOT'] or settings['SAVE_RASTER_PLOT_AS_PNG']:
+    plt.figure()
+    plt.xlabel('Time/ms')
+    plt.ylabel('Neurons')
+    title = 'Testing'
+    if settings['LEARNING']:
+        title = 'Training'
+    title = title + ' - MNIST Classification - ' + str(params['NUM_VR']) + ' Virtual Receptors (VRs)'
+    plt.title(title)
+
+    indexOffset = 0
+    indexOffset = 1 + utils.plotAllSpikes(populationsInput, totalSimulationTime, indexOffset,settings['RECORD_POP_INPUT'])
+    indexOffset = 1 + utils.plotAllSpikes(populationsNoiseSource, totalSimulationTime, indexOffset,settings['RECORD_POP_NOISE_SOURCE'])
+    indexOffset = 1 + utils.plotAllSpikes(populationsRN, totalSimulationTime, indexOffset,settings['RECORD_POP_RN'])
+    indexOffset = 1 + utils.plotAllSpikes(populationsPN, totalSimulationTime, indexOffset,settings['RECORD_POP_PN'])
+    indexOffset = 1 + utils.plotAllSpikes(populationsAN, totalSimulationTime, indexOffset,settings['RECORD_POP_AN'])
+
+#if in the learning stage
+if settings['LEARNING']:
+    #store the weight values learnt via plasticity, these will be reloaded as static weights for test stage
+    classifier.saveLearntWeightsPNAN(settings,params,projectionsPNAN,len(populationsPN),len(populationsAN))
+else:
+    #save the AN layer spike data from the testing run. 
+    #This data will be interrogated to find the winning class (most active AN pop) 
+    #during the presentation of each test observation
+    #classifier.saveSpikesAN(settings,populationsAN)
+    winningClassesByObservation = classifier.calculateWinnersAN(settings,populationsAN)
+    classLabels = utils.loadListFromCsvFile(settings['CLASS_LABELS_PATH'],True)
+    scorePercent = classifier.calculateScore(winningClassesByObservation,classLabels)
+    utils.saveListAsCsvFile(winningClassesByObservation,settings['CLASSIFICATION_RESULTS_PATH'])
+
+#make sure spinnaker shut down before blocking figure is put up
+classifier.end()
+
+if settings['SAVE_RASTER_PLOT_AS_PNG']:
+    filename = 'RasterPlot-Testing.png'
+    if settings['LEARNING']:
+        filename = 'RasterPlot-Training.png'
+    plt.savefig(filename)
+
+#write a marker file to allow invoking programs to know that the Python/Pynn run completed
+utils.saveListToFile(['Pynn Run complete'],settings['RUN_COMPLETE_FILE']) 
+
+if settings['DISPLAY_RASTER_PLOT']:
+    plt.show()
+
+print 'PyNN run completed.'

experiment1/ClassActivation_SpikeSourceData.csv

@@ -0,0 +1,10 @@
+1,4,7,10,13,16,19,22,25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,73,76,79,82,85,88,91,94,97,100,103,106,109,112,115,118,121,124,127,130,133,136,139
+201,204,207,210,213,216,219,222,225,228,231,234,237,240,243,246,249,252,255,258,261,264,267,270,273,276,279,282,285,288,291,294,297,300,303,306,309,312,315,318,321,324,327,330,333,336,339
+401,404,407,410,413,416,419,422,425,428,431,434,437,440,443,446,449,452,455,458,461,464,467,470,473,476,479,482,485,488,491,494,497,500,503,506,509,512,515,518,521,524,527,530,533,536,539
+601,604,607,610,613,616,619,622,625,628,631,634,637,640,643,646,649,652,655,658,661,664,667,670,673,676,679,682,685,688,691,694,697,700,703,706,709,712,715,718,721,724,727,730,733,736,739
+801,804,807,810,813,816,819,822,825,828,831,834,837,840,843,846,849,852,855,858,861,864,867,870,873,876,879,882,885,888,891,894,897,900,903,906,909,912,915,918,921,924,927,930,933,936,939
+1001,1004,1007,1010,1013,1016,1019,1022,1025,1028,1031,1034,1037,1040,1043,1046,1049,1052,1055,1058,1061,1064,1067,1070,1073,1076,1079,1082,1085,1088,1091,1094,1097,1100,1103,1106,1109,1112,1115,1118,1121,1124,1127,1130,1133,1136,1139
+1201,1204,1207,1210,1213,1216,1219,1222,1225,1228,1231,1234,1237,1240,1243,1246,1249,1252,1255,1258,1261,1264,1267,1270,1273,1276,1279,1282,1285,1288,1291,1294,1297,1300,1303,1306,1309,1312,1315,1318,1321,1324,1327,1330,1333,1336,1339
+1401,1404,1407,1410,1413,1416,1419,1422,1425,1428,1431,1434,1437,1440,1443,1446,1449,1452,1455,1458,1461,1464,1467,1470,1473,1476,1479,1482,1485,1488,1491,1494,1497,1500,1503,1506,1509,1512,1515,1518,1521,1524,1527,1530,1533,1536,1539
+1601,1604,1607,1610,1613,1616,1619,1622,1625,1628,1631,1634,1637,1640,1643,1646,1649,1652,1655,1658,1661,1664,1667,1670,1673,1676,1679,1682,1685,1688,1691,1694,1697,1700,1703,1706,1709,1712,1715,1718,1721,1724,1727,1730,1733,1736,1739
+1801,1804,1807,1810,1813,1816,1819,1822,1825,1828,1831,1834,1837,1840,1843,1846,1849,1852,1855,1858,1861,1864,1867,1870,1873,1876,1879,1882,1885,1888,1891,1894,1897,1900,1903,1906,1909,1912,1915,1918,1921,1924,1927,1930,1933,1936,1939

experiment1/ClassLabels.csv

@@ -0,0 +1 @@
+0,1,2,3,4,5,6,7,8,9