"""

Simple test for STDP :

Reproduces a classical plasticity experiment of plasticity induction by

pre/post synaptic pairing specifically :

* At the begining of the simulation, "n_stim_test" external stimulations of

the "pre_pop" (presynaptic) population do not trigger activity in the

"post_pop" (postsynaptic) population.

* Then the presynaptic and postsynaptic populations are stimulated together

"n_stim_pairing" times by an external source so that the "post_pop"

population spikes 10ms after the "pre_pop" population.

* Ater that period, only the "pre_pop" population is externally stimulated

"n_stim_test" times, but now it should trigger activity in the "post_pop"

population (due to STDP learning)

Run as :

$ ./stdp_example

This example requires that the NeuroTools package is installed

(http://neuralensemble.org/trac/NeuroTools)

Authors : Catherine Wacongne < catherine.waco@gmail.com >

Xavier Lagorce < Xavier.Lagorce@crans.org >

April 2013

"""

import pylab

import pyNN.spiNNaker as sim

# SpiNNaker setup

sim.setup(timestep=1.0, min_delay=1.0, max_delay=10.0)

# +-------------------------------------------------------------------+

# | General Parameters |

# +-------------------------------------------------------------------+

# Population parameters

model = sim.IF_curr_exp

cell_params = {'cm': 0.25,

}

# Other simulation parameters

e_rate = 80

in_rate = 300

n_stim_test = 5

n_stim_pairing = 20

dur_stim = 20

pop_size = 40

ISI = 90.

start_test_pre_pairing = 200.

start_pairing = 1500.

start_test_post_pairing = 700.

simtime = (start_pairing + start_test_post_pairing

+ ISI * (n_stim_pairing + n_stim_test) + 550.)

# Initialisations of the different types of populations

IAddPre = []

IAddPost = []

# +-------------------------------------------------------------------+

# | Creation of neuron populations |

# +-------------------------------------------------------------------+

# Neuron populations

pre_pop = sim.Population(pop_size, model, cell_params)

post_pop = sim.Population(pop_size, model, cell_params)

# Test of the effect of activity of the pre_pop population on the post_pop

# population prior to the "pairing" protocol : only pre_pop is stimulated

for i in range(n_stim_test):

IAddPre.append(sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': in_rate,

'start': start_test_pre_pairing + ISI * (i),

'duration': dur_stim

}))

# Pairing protocol : pre_pop and post_pop are stimulated with a 10 ms

# difference

for i in range(n_stim_pairing):

IAddPre.append(sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': in_rate,

'start': start_pairing + ISI * (i),

'duration': dur_stim

}))

IAddPost.append(sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': in_rate,

'start': start_pairing + ISI * (i) + 10.,

'duration': dur_stim

}))

# Test post pairing : only pre_pop is stimulated (and should trigger activity

# in Post)

for i in range(n_stim_test):

IAddPre.append(sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': in_rate,

'start': (start_pairing

+ ISI * (n_stim_pairing)

+ start_test_post_pairing

+ ISI * (i)),

'duration': dur_stim

}))

# Noise inputs

INoisePre = sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': e_rate, 'start': 0, 'duration': simtime},

label="expoisson")

INoisePost = sim.Population(pop_size,

sim.SpikeSourcePoisson,

{'rate': e_rate, 'start': 0, 'duration': simtime},

label="expoisson")

# +-------------------------------------------------------------------+

# | Creation of connections |

# +-------------------------------------------------------------------+

# Connection parameters

JEE = 3.

# Connection type between noise poisson generator and excitatory populations

ee_connector = sim.OneToOneConnector(weights=JEE * 0.05)

# Noise projections

sim.Projection(INoisePre, pre_pop, ee_connector, target='excitatory')

sim.Projection(INoisePost, post_pop, ee_connector, target='excitatory')

# Additional Inputs projections

for i in range(len(IAddPre)):

sim.Projection(IAddPre[i], pre_pop, ee_connector, target='excitatory')

for i in range(len(IAddPost)):

sim.Projection(IAddPost[i], post_pop, ee_connector, target='excitatory')

# Plastic Connections between pre_pop and post_pop

stdp_model = sim.STDPMechanism(

)

plastic_projection = sim.Projection(

pre_pop, post_pop, sim.FixedProbabilityConnector(p_connect=0.5),

synapse_dynamics=sim.SynapseDynamics(slow=stdp_model)

)

# +-------------------------------------------------------------------+

# | Simulation and results |

# +-------------------------------------------------------------------+

# Record neurons' potentials

pre_pop.record_v()

post_pop.record_v()

# Record spikes

pre_pop.record()

post_pop.record()

# Run simulation

sim.run(simtime)

print("Weights:", plastic_projection.getWeights())

def plot_spikes(spikes, title):

print "No spikes received"

pre_spikes = pre_pop.getSpikes(compatible_output=True)

post_spikes = post_pop.getSpikes(compatible_output=True)

plot_spikes(pre_spikes, "pre-synaptic")

plot_spikes(post_spikes, "post-synaptic")

pylab.show()

# End simulation on SpiNNaker

sim.end()