In [1]:

    

import pyNN.neuron as p
p.setup(timestep=0.1, quit_on_end=False)


    

    




/home/mi/k0022172/mand_course/lib/python2.7/site-packages/pyNN/random.py:25: UserWarning: GSL random number generators not available
  warnings.warn("GSL random number generators not available")

In [2]:

    

FileNames_byPeak = ('data/by_peak_SNR/bootstrap_joe093-3-C3-MO.mat',
                    'data/by_peak_SNR/bootstrap_joe145-4-C3-MO.mat',
                    'data/by_peak_SNR/bootstrap_joe112-5-C3-MO.mat',
                    'data/by_peak_SNR/bootstrap_joe112-6-C3-MO.mat',
                    'data/by_peak_SNR/bootstrap_joe108-7-C3-MO.mat')

'''
5 neurons selected by peak SNR

time offset: 'Movement Onset' at time 0
'''


    

    
Out[2]:





"\n5 neurons selected by peak SNR\n\ntime offset: 'Movement Onset' at time 0\n"

In [3]:

    

import scipy.io

''' 6 neurons selected by average SNR

** DATA FORMAT **

one file - one neuron

Example:
data/TS_by_avg_SNRbootstrap_joe115-2-C1-TS.mat
    monkey: joe
    session: 115
    neuron: 2
    condition: C1
    TS: trial start
    
Each file stores 6 directions
Within each direction there are 35 trials

* TEMPORAL STRUCTURE *
trial start (t=0ms)
cue       (t=500ms)
go       (t=1500ms)
movement onset (MO)
movemenr end   (ME)

'''

# Load Data
FileNames = ('data/TS_by_avg_SNRbootstrap_joe115-2-C1-TS.mat',
             'data/TS_by_avg_SNRbootstrap_joe115-3-C1-TS.mat',
             'data/TS_by_avg_SNRbootstrap_joe149-4-C1-TS.mat',
             'data/TS_by_avg_SNRbootstrap_joe096-5-C1-TS.mat',
             'data/TS_by_avg_SNRbootstrap_joe110-5-C1-TS.mat',
             'data/TS_by_avg_SNRbootstrap_joe113-6-C1-TS.mat')

# Data is a list with an item for every neuron
data = [scipy.io.loadmat(filename, squeeze_me = True)['GDFcell'].tolist() for filename in FileNames]


    

In [4]:

    

# Parameters
N = 6 #number of neurons in input population
Ntrials = 35 #1-25 Training, 26-35 Testing
Ndirections = 6

# Select Data
direction = 1
trial = 1


    

In [5]:

    

spikes_one_direction = [dat[direction] for dat in data]

# Spike Times is a list with an item for every neuron
# The items are list of spiketimes for selected direction and trial
spike_times = [spikes[spikes[:,0] == trial][:,1] for spikes in spikes_one_direction]


    

In [6]:

    

# Define Population
inpop = p.Population(N, cellclass=p.SpikeSourceArray)

L1 = p.Population(N, cellclass=p.IF_cond_exp)

L1.record()
L1.record_v()

# Load Spike times into the input population
for neuron,times in zip(inpop,spike_times):
    neuron.spike_times = sort(times)


    

In [7]:

    

inpop[0].spike_times


    

    
Out[7]:





array([  650.,   717.,   963.,  1045.,  1168.,  1301.,  1463.])

In [160]:

    

# connections
prj = p.Projection(inpop, L1, target="excitatory", method=p.OneToOneConnector())
prj.setWeights(0.1)


    

In [161]:

    

# run sim
p.reset()
p.run(2000)


    

    
Out[161]:





2000.0000000050845

In [162]:

    

# get recordings
vL1dat = L1.get_v()


time = vL1dat[:,1].reshape(N,-1)[0]
VL1 = vL1dat[:,2].reshape(N,-1).T

L1_spikes = L1.getSpikes()


    

In [163]:

    

# plot
subplot(2,1,1)
plot(time, VL1)
ylabel('post1: V[mV]')

subplot(2,1,2)
plot(L1_spikes[:,1], L1_spikes[:,0],'.')


    

    
Out[163]:





[<matplotlib.lines.Line2D at 0xc27aa10>]