AdEx network + synapses#

The Dendrify implementation of the Adaptive exponential integrate-and-fire model (adapted from Brian’s examples).

In this example, we also explore:

How to add random Poisson synaptic input.
How to create a basic network model.

Resources:

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

from dendrify import PointNeuronModel

b.prefs.codegen.target = 'numpy'  # faster for simple simulations
b.seed(1234)  # for reproducibility

# Create neuron model and add AMPA equations
model = PointNeuronModel(model='adex',
                         cm_abs=281*pF,
                         gl_abs=30*nS,
                         v_rest=-70.6*mV)
model.synapse('AMPA', tag='x', g=0.5*nS, t_decay=2*ms)
model.synapse('NMDA', tag='x', g=0.5*nS, t_decay=50*ms)

# Include adex parameters
model.add_params({'Vth': -50.4*mV,
                  'DeltaT': 2*mV,
                  'tauw': 144*ms,
                  'a': 4*nS,
                  'b': 0.0805*nA,
                  'Vr': -70.6*mV})

# Create a NeuronGroup
neuron = model.make_neurongroup(N=100, threshold='V>Vth+5*DeltaT',
                                reset='V=Vr; w+=b',
                                method='euler')

# Create a Poisson input
Input = b.PoissonGroup(200, rates=100*Hz)

# Randomly connect Poisson input to NeuronGroup
S = b.Synapses(Input, neuron, on_pre='s_AMPA_x += 1; s_NMDA_x += 1')
S.connect(p=0.25)

# Record voltages and spike times
trace = b.StateMonitor(neuron, 'V', record=0)
spikes = b.SpikeMonitor(neuron)

# Run simulation
b.run(200 * ms)

# Trick to draw nicer spikes in I&F models
vm = trace[0].V[:]
for t in spikes.spike_trains()[0]:
    i = int(t / b.defaultclock.dt)
    vm[i] = 20*mV

# Plot results
fig, axes = b.subplots(2, 1, figsize=[6, 6])
ax1, ax2 = axes
ax1.plot(trace.t / ms, vm / mV, label='$V_0$')
ax1.set_ylabel('Voltage (mV)')
ax1.legend()
ax2.plot(spikes.t/ms, spikes.i, '.', label='spikes')
ax2.set_xlabel('Time (ms)')
ax2.set_ylabel('Neuron index')
ax2.legend()
fig.tight_layout()
b.show()