StaticPulseDendriticDelay is not working

[FabianSchubert]

The delay kernel provided to StaticPulseDendriticDelay has no effect.

Example:

#! /usr/bin/env python3

import numpy as np
import matplotlib.pyplot as plt

from pygenn import genn_model
from pygenn.genn_model import GeNNModel, create_custom_neuron_class
from pygenn.genn_wrapper import NO_DELAY

model_input_spike = create_custom_neuron_class(
        "inp_neur",
        param_names=["rate"],
        var_name_types=[("t_spk", "scalar")],
        sim_code="""
        """,
        threshold_condition_code="$(t) - $(t_spk) >= 1.0/$(rate)",
        reset_code="$(t_spk) = $(t);",
        is_auto_refractory_required=False)

model_output_li = create_custom_neuron_class(
    "output_neur",
    param_names=["tau"],
    var_name_types=[("v", "scalar")],
    sim_code="""
    $(v) += DT * ($(Isyn) - $(v));
    """
)

DT = 0.1

# just to get some different levels of inhibition
KERNEL_G = -np.array([.0, .2, .2, .2, .0,
                      .2, .5, .5, .5, .2,
                      .2, .5, 1., .5, .2,
                      .2, .5, .5, .5, .2,
                      .0, .2, .2, .2, .0])

T_DELAY = 1.5
N_DELAY_STEPS = int(T_DELAY / DT)

KERNDEL_D = np.ones((25)).astype("int") * N_DELAY_STEPS

syn_vars = {
    "g": KERNEL_G,
    "d": KERNDEL_D
}

kernel_params = {
    "conv_kh": 5,
    "conv_kw": 5,
    "conv_ih": 1,
    "conv_iw": 1,
    "conv_ic": 1,
    "conv_oh": 5,
    "conv_ow": 5,
    "conv_oc": 1
}

kernel_conn = genn_model.init_toeplitz_connectivity("Conv2D", kernel_params)

model = GeNNModel("float", "test")
model.dT = DT

P = model.add_neuron_population("input", 1, model_input_spike,
                                {"rate": 1.0}, {"t_spk": 0.0})

S = model.add_neuron_population("output", 25, model_output_li,
                                {"tau": 1.0}, {"v": 0.0})

syn_SP_E = model.add_synapse_population("exc", "DENSE_GLOBALG", NO_DELAY,
                                        P, S, "StaticPulse",
                                        {}, {"g": 1.0},
                                        {}, {}, "DeltaCurr",
                                        {}, {})

syn_SP_I = model.add_synapse_population("inh", "TOEPLITZ_KERNELG", NO_DELAY,
                                        P, S, "StaticPulseDendriticDelay", {}, syn_vars,
                                        {}, {}, "DeltaCurr", {}, {}, kernel_conn)

P.spike_recording_enabled = True

T = 10.0
NT = int(T/model.dT)
T = NT * model.dT

model.build()
model.load(num_recording_timesteps=NT)

t_ax = np.linspace(0., T, NT, endpoint=False)

sv_rec = np.ndarray((NT, 25))

for t in range(NT):

    model.step_time()

    S.pull_var_from_device("v")
    sv_rec[t,:] = S.vars["v"].view

model.pull_recording_buffers_from_device()

sp_t, _ = P.spike_recording_data

plt.plot(t_ax, sv_rec)
plt.vlines(sp_t, sv_rec.min(), sv_rec.max(), linestyles='--', label="input spikes")
plt.xlabel("t")
plt.ylabel("Output Population V")
plt.legend()

plt.show()

Here, inhibition should be delayed, but arrives at the exact same time as the excitation.

[neworderofjamie]

You need to set the maximum number of dendritic delay slots with something like:

 syn_SP_I.pop.set_max_dendritic_delay_timesteps(N_DELAY_STEPS)

[FabianSchubert]

This still results in the same model output

[FabianSchubert]

Fixed: syn_SP_I.pop.set_max_dendritic_delay_timesteps(N_DELAY_STEPS + 1) maximum number of delay steps have to be 1 larger than the largest number of steps in the kernel.