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SC-NeuroCore Documentation

Spike Debugger

Tutorial 42: Spike-Level Debugging¶

When your SNN gives wrong answers, the spike debugger tells you where and why. Record full execution traces, compare two runs, find the first divergence point, and trace causal spike chains backward through time.

1. Record an Execution Trace¶

from sc_neurocore.debug import SpikeTracer
from sc_neurocore.network.population import Population
from sc_neurocore.network.network import Network
from sc_neurocore.network.stimulus import PoissonInput
from sc_neurocore.network.monitor import SpikeMonitor
from sc_neurocore.neurons.models.hodgkin_huxley import HodgkinHuxleyNeuron

pop = Population(HodgkinHuxleyNeuron, n=10, label="hh")
drive = PoissonInput(n=10, rate_hz=200.0, weight=8.0, dt=0.001, seed=42)
net = Network(pop, drive, SpikeMonitor(pop))

tracer = SpikeTracer(net)
trace = tracer.run(duration=0.05, dt=0.001)
print(f"{trace.n_neurons} neurons, {trace.n_steps} steps, {trace.spike_count} spikes")

2. Inspect a Single Neuron¶

nt = trace.neuron_trace(0)
print(f"Neuron 0: {len(nt['spike_times'])} spikes")
print(f"  Voltage: [{nt['voltages'].min():.1f}, {nt['voltages'].max():.1f}]")

3. Find Divergence Between Two Runs¶

from sc_neurocore.debug import find_divergence

# Second run with different seed
pop2 = Population(HodgkinHuxleyNeuron, n=10, label="hh2")
net2 = Network(pop2, PoissonInput(n=10, rate_hz=200.0, weight=8.0, dt=0.001, seed=99), SpikeMonitor(pop2))
trace2 = SpikeTracer(net2).run(duration=0.05, dt=0.001)

div = find_divergence(trace, trace2)
if div:
    print(f"Divergence at t={div.timestep}, neuron={div.neuron_id}, dV={div.voltage_diff:.2f}")

4. Trace Causal Chain¶

from sc_neurocore.debug import causal_chain

spike_t = trace.spike_times(0)
if len(spike_t) > 0:
    chain = causal_chain(trace, neuron_id=0, timestep=int(spike_t[0]), max_depth=5)
    for e in chain:
        print(f"  t={e.timestep}, n={e.neuron_id}, I={e.input_current:.2f}, spike={e.spiked}")

Use Cases¶

ANN-to-SNN conversion: find where spikes diverge from expected
Hardware verification: compare Python vs Verilog output
Training diagnostics: identify dead or saturated neurons

Further Reading¶