Clopath Rule: Spike pairing experiment¶

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This script simulates one aeif_psc_delta_clopath neuron that is connected with a Clopath connection [1]. The synapse receives pairs of a pre- and a postsynaptic spikes that are separated by either 10 ms (pre before post) or -10 ms (post before pre). The change of the synaptic weight is measured after five of such pairs. This experiment is repeated five times with different rates of the sequence of the spike pairs: 10Hz, 20Hz, 30Hz, 40Hz, and 50Hz.

References¶

import matplotlib.pyplot as plt
import nest
import numpy as np

First we specify the neuron parameters. To enable voltage dependent prefactor A_LTD(u_bar_bar) add A_LTD_const: False to the dictionary.

nrn_params = {
    "V_m": -70.6,
    "E_L": -70.6,
    "C_m": 281.0,
    "theta_minus": -70.6,
    "theta_plus": -45.3,
    "A_LTD": 14.0e-5,
    "A_LTP": 8.0e-5,
    "tau_u_bar_minus": 10.0,
    "tau_u_bar_plus": 7.0,
    "delay_u_bars": 4.0,
    "a": 4.0,
    "b": 0.0805,
    "V_reset": -70.6 + 21.0,
    "V_clamp": 33.0,
    "t_clamp": 2.0,
    "t_ref": 0.0,
}

Hardcoded spike times of presynaptic spike generator

spike_times_pre = [
    # Presynaptic spike before the postsynaptic
    [20.0, 120.0, 220.0, 320.0, 420.0],  # noqa
    [20.0, 70.0, 120.0, 170.0, 220.0],  # noqa
    [20.0, 53.3, 86.7, 120.0, 153.3],  # noqa
    [20.0, 45.0, 70.0, 95.0, 120.0],  # noqa
    [20.0, 40.0, 60.0, 80.0, 100.0],  # noqa
    # Presynaptic spike after the postsynaptic
    [120.0, 220.0, 320.0, 420.0, 520.0, 620.0],  # noqa
    [70.0, 120.0, 170.0, 220.0, 270.0, 320.0],  # noqa
    [53.3, 86.6, 120.0, 153.3, 186.6, 220.0],  # noqa
    [45.0, 70.0, 95.0, 120.0, 145.0, 170.0],  # noqa
    [40.0, 60.0, 80.0, 100.0, 120.0, 140.0],
]  # noqa

Hardcoded spike times of postsynaptic spike generator

spike_times_post = [
    [10.0, 110.0, 210.0, 310.0, 410.0],  # noqa
    [10.0, 60.0, 110.0, 160.0, 210.0],  # noqa
    [10.0, 43.3, 76.7, 110.0, 143.3],  # noqa
    [10.0, 35.0, 60.0, 85.0, 110.0],  # noqa
    [10.0, 30.0, 50.0, 70.0, 90.0],  # noqa
    [130.0, 230.0, 330.0, 430.0, 530.0, 630.0],  # noqa
    [80.0, 130.0, 180.0, 230.0, 280.0, 330.0],  # noqa
    [63.3, 96.6, 130.0, 163.3, 196.6, 230.0],  # noqa
    [55.0, 80.0, 105.0, 130.0, 155.0, 180.0],  # noqa
    [50.0, 70.0, 90.0, 110.0, 130.0, 150.0],
]  # noqa
init_w = 0.5
syn_weights = []
resolution = 0.1

Loop over pairs of spike trains

for s_t_pre, s_t_post in zip(spike_times_pre, spike_times_post):
    nest.ResetKernel()
    nest.resolution = resolution

    # Create one neuron
    nrn = nest.Create("aeif_psc_delta_clopath", 1, nrn_params)

    # We need a parrot neuron since spike generators can only
    # be connected with static connections
    prrt_nrn = nest.Create("parrot_neuron", 1)

    # Create and connect spike generators
    spike_gen_pre = nest.Create("spike_generator", {"spike_times": s_t_pre})

    nest.Connect(spike_gen_pre, prrt_nrn, syn_spec={"delay": resolution})

    spike_gen_post = nest.Create("spike_generator", {"spike_times": s_t_post})

    nest.Connect(spike_gen_post, nrn, syn_spec={"delay": resolution, "weight": 80.0})

    # Create weight recorder
    wr = nest.Create("weight_recorder")

    # Create Clopath connection with weight recorder
    nest.CopyModel("clopath_synapse", "clopath_synapse_rec", {"weight_recorder": wr})
    syn_dict = {"synapse_model": "clopath_synapse_rec", "weight": init_w, "delay": resolution}
    nest.Connect(prrt_nrn, nrn, syn_spec=syn_dict)

    # Simulation
    simulation_time = 10.0 + max(s_t_pre[-1], s_t_post[-1])
    nest.Simulate(simulation_time)

    # Extract and save synaptic weights
    weights = wr.get("events", "weights")
    syn_weights.append(weights[-1])

syn_weights = np.array(syn_weights)
# scaling of the weights so that they are comparable to [1]
syn_weights = 100.0 * 15.0 * (syn_weights - init_w) / init_w + 100.0

# Plot results
fig, ax = plt.subplots(1, sharex=False)
ax.plot([10.0, 20.0, 30.0, 40.0, 50.0], syn_weights[5:], color="b", lw=2.5, ls="-", label="pre-post pairing")
ax.plot([10.0, 20.0, 30.0, 40.0, 50.0], syn_weights[:5], color="g", lw=2.5, ls="-", label="post-pre pairing")
ax.set_ylabel("normalized weight change")
ax.set_xlabel("rho (Hz)")
ax.legend()
ax.set_title("synaptic weight")

plt.show()

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