scaley = int(np.round(training['cols']/resolution))
print(resolution)
num_examples = 4.0 #len(training)
j = 0
dat_arr = []
while j < (int(num_examples)):
print('image: ' + str(j))
rates = training['x'][j%60000,:,:] / 8.*input_intensity
downsample = skimage.transform.downscale_local_mean(rates, (scalex, scaley))
print('downsampled image from (28, 28) to ' + str(downsample.shape))
linear = np.ravel(downsample)
neuron_inds, current_vals = add_current(AL, linear, Nglo, Npn)
ex.const_current(AL, Nglo, neuron_inds, current_vals)
#set up the lab
f, initial_conditions, neuron_inds = lm.set_up_lab(AL)
time_sampled_range = np.arange(0., time_per_image*1000, dt*1000)
data = lm.run_lab(f, initial_conditions, time_sampled_range, integrator = 'dopri5',compile=True)
dat_arr.append(data)
lm.show_random_neuron_in_layer(time_sampled_range,data,AL,1,2)
lm.show_random_neuron_in_layer(time_sampled_range,data,AL,3,2)
lm.show_random_neuron_in_layer(time_sampled_range,data,AL,5,2)
j+=1
np.save('MNIST_AL_data.npy', dat_arr)
gLNPN=200,
gPN=300)
#AL = net.create_AL_man(nm.LN, nm.PN_2, nm.Synapse_gaba_LN, nm.Synapse_nAch_PN_2)
#Set up the experiment
num_layers = 2
#The value of the input current is 400 pA
val = 250 #nA
#val = 400
#These are the neuron indicies within each layer which receive the current
neuron_inds = [[0, 1], [1, 3]]
current_vals = [[val, val], [val, val]]
# Set up the experiment with a constant input current
ex.const_current(AL, num_layers, neuron_inds, current_vals)
#set up the lab
f, initial_conditions, neuron_inds = lm.set_up_lab(AL)
#run for specified time with dt
time_len = 1000.0 #Run for 600 ms
dt = 0.02 # Integration time step 0.02 ms
time_sampled_range = np.arange(0., time_len, dt)
# Run the lab and get output
data = lm.run_lab(f,
initial_conditions,
time_sampled_range,
integrator='dopri5',
compile=True)
stim = pickle.load(stimuli_pickle)
stimuli_pickle.close()
# Set the current value -- assumes a dc current for all neurons
current_amp = stim_amp(conc)
# Numer of glomeruli
num_layers = len(AL.layers)
#adj_m = net.get_edges_data(AL, "weight")
curr_inds = stim[0][odor]
curr_vals = current_amp * np.asarray(stim[1][odor])
stim = None
print('Odor: {0}, Concentration {1}, Trial {2}'.format(odor, conc, trial))
ex.const_current(AL, num_layers, curr_inds, curr_vals)
# set up the lab
f, initial_conditions, all_neuron_inds = lm.set_up_lab(AL)
# run the lab
data = lm.run_lab(f,
initial_conditions,
time_sampled_range,
integrator='dopri5',
compile=True)
data = np.transpose(data)
# Isolate Projection Neuron and Local Neuron indicies for use elsewhere
pn_inds = np.concatenate(np.array(