from axon_sdk.primitives import (
SpikingNetworkModule,
DataEncoder,
)
from typing import Optional
# Defining constant value here
T_F = 50.0
[docs]
class LogNetwork(SpikingNetworkModule):
def __init__(self, encoder: DataEncoder, module_name: Optional[str] = None):
super().__init__(module_name)
self.encoder = encoder
# Constants
Vt = 10.0
tm = 100.0
self.tf = 20.0 # Increasing this makes Log more accurate
Tsyn = 1.0
Tmin = encoder.Tmin
we = Vt
wi = -Vt
gmult = (Vt * tm) / self.tf
wacc_bar = (Vt * tm) / encoder.Tcod
# Neurons
self.input = self.add_neuron(Vt=Vt, tm=tm, tf=self.tf, neuron_name="input")
self.first = self.add_neuron(Vt=Vt, tm=tm, tf=self.tf, neuron_name="first")
self.last = self.add_neuron(Vt=Vt, tm=tm, tf=self.tf, neuron_name="last")
self.acc = self.add_neuron(Vt=Vt, tm=tm, tf=self.tf, neuron_name="acc")
self.output = self.add_neuron(Vt=Vt, tm=tm, tf=self.tf, neuron_name="output")
# Input triggers
self.connect_neurons(self.input, self.first, "V", we, Tsyn)
self.connect_neurons(self.input, self.last, "V", 0.5 * we, Tsyn)
# Self-inhibit first after spike
self.connect_neurons(self.first, self.first, "V", wi, Tsyn)
# Encoding into acc from first and last
self.connect_neurons(self.first, self.acc, "ge", wacc_bar, Tsyn + Tmin)
self.connect_neurons(self.last, self.acc, "gate", 1.0, Tsyn)
self.connect_neurons(self.last, self.acc, "ge", -wacc_bar, Tsyn)
self.connect_neurons(self.last, self.acc, "gf", gmult, Tsyn)
# Output driven by acc spike
self.connect_neurons(self.acc, self.output, "V", we, Tsyn + Tmin)
self.connect_neurons(self.last, self.output, "V", we, 2 * Tsyn)
[docs]
def expected_log_output_delay(x, encoder: DataEncoder, tf: float):
import math
Tin = encoder.Tmin + x * encoder.Tcod
try:
delay = tf * math.log(encoder.Tcod / (Tin - encoder.Tmin))
Tout = encoder.Tmin + delay
return Tout
except ValueError:
return float("nan")
[docs]
def decode_logarithm(output_interval, encoder: DataEncoder, tf: float):
return (encoder.Tmin - output_interval) / tf
if __name__ == "__main__":
import math
from axon_sdk import Simulator
encoder = DataEncoder(Tmin=10.0, Tcod=100.0)
lognet = LogNetwork(encoder, module_name="lognet")
val = 0.389
sim = Simulator(lognet, encoder, dt=0.01)
sim.apply_input_value(val, neuron=lognet.input, t0=10)
sim.simulate(300)
output_spikes = sim.spike_log.get(lognet.output.uid, [])
acc_spikes = sim.spike_log.get(lognet.acc.uid, [])
last_spike_time = sim.spike_log.get(lognet.last.uid, [None])[-1]
print(f"Input value: {val}, log result: {math.log(val)}")
print(f"Expected delay (log({val})): {expected_log_output_delay(val, encoder, lognet.tf):.3f} ms")
if len(output_spikes) >= 2:
interval = output_spikes[1] - output_spikes[0]
actual_value = decode_logarithm(interval, encoder, lognet.tf)
print(
f"✅ Output spike interval: {interval:.3f} ms, corresponding to {actual_value}"
)
elif len(output_spikes) == 1:
delay = output_spikes[0] - last_spike_time if last_spike_time else float("nan")
print(
f"✅ Output spike at: {output_spikes[0]:.3f} ms (delay from 'last': {delay:.3f} ms)"
)
else:
print("❌ No output spike detected.")