Networks of compartmental neuronsΒΆ

In this example we show:

  • How to create a recurrent network of compartmental neurons.

  • How active dendrites can alter network responses.

import brian2 as b
from brian2.units import Hz, ms, mV, nS, pF

from dendrify import Dendrite, NeuronModel, Soma

b.prefs.codegen.target = 'numpy'  # faster for simple simulations

# Create neuron model with passive dendrites
soma = Soma('soma', cm_abs=200*pF, gl_abs=10*nS)
dend = Dendrite('dend', cm_abs=50*pF, gl_abs=2.5*nS)
dend.synapse('AMPA', tag='external', g=1*nS,  t_decay=5*ms)
dend.synapse('AMPA', tag='recurrent', g=1*nS,  t_decay=5*ms)
model_passive = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)

# Add dendritic spikes and create a neuron model with active dendrites
dend.dspikes('Na', g_rise=30*nS, g_fall=14*nS)
model_active = NeuronModel([(soma, dend, 15*nS)], v_rest=-60*mV)
model_active.config_dspikes(
    'Na', threshold=-35*mV,
    duration_rise=1.2*ms, duration_fall=2.4*ms,
    offset_fall=0.2*ms, refractory=5*ms,
    reversal_rise='E_Na', reversal_fall='E_K')

# Create a neuron group with passive dendrites
neuron_passive, reset_p = model_passive.make_neurongroup(
    200, method='euler',
    threshold='V_soma > -40*mV',
    reset='V_soma = 40*mV',
    second_reset='V_soma=-50*mV',
    spike_width=0.8*ms,
    refractory=4*ms)

# Create a neuron group with active dendrites
neuron_active, reset_a = model_active.make_neurongroup(
    200, method='euler',
    threshold='V_soma > -40*mV',
    reset='V_soma = 40*mV',
    second_reset='V_soma=-50*mV',
    spike_width=0.8*ms,
    refractory=4*ms)

# Create random Poisson input
# Protocol: five 50 ms blocks of 50 Hz stimulation, followed by 50 ms of silence
stimulus = b.TimedArray(b.tile([50., 0.], 5)*Hz, dt=50.*ms)
Input_a = b.PoissonGroup(200, rates='stimulus(t)')
Input_p = b.PoissonGroup(200, rates='stimulus(t)')

# Create synapses for external input
S_input_passive = b.Synapses(
    Input_p, neuron_passive,
    on_pre='s_AMPA_external_dend += 1')
S_input_passive.connect(p=0.2)

S_input_active = b.Synapses(
    Input_a, neuron_active,
    on_pre='s_AMPA_external_dend += 1')
S_input_active.connect(p=0.2)

# Create recurrent synapses
S_recurrent_passive = b.Synapses(
    neuron_passive, neuron_passive,
    on_pre='s_AMPA_recurrent_dend += 1')
S_recurrent_passive.connect(p=0.1)

S_recurrent_active = b.Synapses(
    neuron_active, neuron_active,
    on_pre='s_AMPA_recurrent_dend += 1')
S_recurrent_active.connect(p=0.1)

# Record spikes
spikes_passive = b.SpikeMonitor(neuron_passive)
spikes_active = b.SpikeMonitor(neuron_active)

# Run simulation
b.seed(123)  # for reproducibility
net_passive = b.Network(neuron_passive, reset_p, Input_p,
                        S_input_passive, S_recurrent_passive,
                        spikes_passive)
net_passive.run(550*ms)
b.start_scope()  # clear previous simulation
b.seed(123)  # for reproducibility
net_active = b.Network(neuron_active, reset_a, Input_a,
                       S_input_active, S_recurrent_active,
                       spikes_active)
net_active.run(550*ms)

# Visualize results
b.figure(figsize=[6, 5])
b.plot(spikes_passive.t/ms, spikes_passive.i + 200,
       '.', ms=3, label='passive dendrites')
b.plot(spikes_active.t/ms, spikes_active.i,
       '.', ms=3, c='C3', label='active dendrites')
b.xlabel('Time (ms)')
b.ylabel('Neuron index')
b.legend()
b.tight_layout()
b.show()
../_images/comp_network.png