Leaky Integrate and Fire Neuron PymoNNto Implementation

The following code creates a network with 800 excitatory neurons, 200 inhibitory neurons and all the connections between them. The neurons are simple leaky integrate and fire neurons which receive inhibitory and excitatory input.

from PymoNNto import *

class LIF_main(Behaviour):

    def set_variables(self, n):
        self.set_init_attrs_as_variables(n)
        n.v = n.get_neuron_vec('uniform')*n.v_rest
        n.fired = n.get_neuron_vec() > 0
        n.dt = 0.1

    def new_iteration(self, n):
        n.v += ((n.v_rest-n.v)+n.I)*n.dt

        n.fired = n.v > n.v_threshold
        if np.sum(n.fired) > 0:
            n.v[n.fired] = n.v_reset


class LIF_input(Behaviour):

    def set_variables(self, n):
        for s in n.afferent_synapses['All']:
            s.W = s.get_synapse_mat('uniform')

        n.I = n.get_neuron_vec()

    def new_iteration(self, n):

        n.I = 90 * n.get_neuron_vec('uniform')

        for s in n.afferent_synapses['GLUTAMATE']:
            n.I += np.sum(s.W[:, s.src.fired], axis=1)

        for s in n.afferent_synapses['GABA']:
            n.I -= np.sum(s.W[:, s.src.fired], axis=1)




My_Network = Network()

N_e = NeuronGroup(net=My_Network, tag='excitatory_neurons', size=get_squared_dim(800), behaviour={
    1: LIF_main(v_rest=-65, v_reset=-80, v_threshold=-10),
    2: LIF_input(),
    9: Recorder(tag='my_recorder', variables=['n.v', 'n.fired'])
})

N_i = NeuronGroup(net=My_Network, tag='inhibitory_neurons', size=get_squared_dim(200), behaviour={
    1: LIF_main(v_rest=-65, v_reset=-80, v_threshold=-30),
    2: LIF_input(),
    9: Recorder(tag='my_recorder', variables=['n.v', 'n.fired'])
})

SynapseGroup(net=My_Network, src=N_e, dst=N_e, tag='GLUTAMATE')
SynapseGroup(net=My_Network, src=N_e, dst=N_i, tag='GLUTAMATE')
SynapseGroup(net=My_Network, src=N_i, dst=N_e, tag='GABA')
SynapseGroup(net=My_Network, src=N_i, dst=N_i, tag='GABA')

My_Network.initialize()

My_Network.simulate_iterations(1000, measure_block_time=True)

import matplotlib.pyplot as plt
plt.plot(My_Network['n.v', 0])
plt.show()

plt.imshow(My_Network['n.fired', 0, 'np'].transpose(),cmap='gray', aspect='auto')
plt.show()


#from PymoNNto.Exploration.Network_UI import *
#my_UI_modules = get_default_UI_modules(['fired', 'v', 'u'], ['W'])
#Network_UI(My_Network, modules=my_UI_modules, label='My_Network_UI', group_display_count=2).show()