Synapse Parameter in Probe

Could anyone explain to me the difference between these two below probes?

out_p = nengo.Probe(out, label="out_p")
out_p_filt = nengo.Probe(out, synapse=0.1, label="out_p_filt")

In the document was told that one without a filter and the other with the filter but I didn’t understand very well.
Is the synapse parameter the same as synapse weight?

Hello @arminsoltan, welcome to our community!

synapse parameter is used for smoothing the input/output signal; and the signal can be anything i.e. a continuous or discrete signal (e.g. spikes which are discrete). In the code below I have tried to explain the effect of synapse parameter.

import nengo
import matplotlib.pyplot as plt

# `spike_generator()` generates spikes of amplitude 10.0 every 5th timestep.
def spike_generator(t, freq=5):
    if (int(t*1000.0) % freq == 0): # Note: `t` starts at 0.001.
        return 10.0
        return 0.0

# Create the Nengo Network.
with nengo.Network() as net:
    sg = nengo.Node(output=spike_generator, size_out=1)
    probe1 = nengo.Probe(sg, synapse=None) # Unfiltered Spikes.
    probe2 = nengo.Probe(sg, synapse=0.005) # Filtered Spikes (i.e. somewhat smoothed spikes)

with nengo.Simulator(net) as sim: # Run it for 200 timesteps.

# Plot the probe results.
ax1 = plt.subplot(2, 1, 1)
ax1.set_title("Un-Filtered Spikes")
ax2 = plt.subplot(2, 1, 2)
ax2.set_title("Filtered Spikes")


As you can see above, on plotting the probe2 results, we get a somewhat smoothed signal compared to the output of probe1 results (where the synapse is set to None). Try playing with the synapse parameter in line probe2 = nengo.Probe(sg, synapse=0.005) by setting it to different values i.e. 0.1, 0.001 and so on… to see its effects.

More technically, synapse parameter applies a synaptic filtering with a default Low Pass filter (i.e. nengo.Lowpass(). More info here. It is not the same as synaptic weight. You rather set the connection “weights” via the transform parameter of nengo.Connection() (more info here).