File size: 4,025 Bytes
e4cdd5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | """Temporal Patterns Benchmark
==============================
Demonstrates P17 axon delays for temporal pattern detection.
A source population sends spikes through connections with varying delays,
causing target neurons to receive coincident inputs at different times.
Features demonstrated: Axon delays, spike timing, temporal coding.
"""
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import neurocore as nc
from neurocore.constants import NEURONS_PER_CORE
def main():
print("=" * 60)
print(" Temporal Pattern Detection Benchmark (P17 Delays)")
print("=" * 60)
net = nc.Network()
# Input neurons fire at different times via stimulus timing
inputs = net.population(4, params={"threshold": 100, "leak": 0, "refrac": 5},
label="inputs")
# Coincidence detector: fires only when multiple delayed inputs arrive together
detector = net.population(1, params={"threshold": 800, "leak": 50, "refrac": 3},
label="detector")
# Each input has a different delay so they arrive at the detector simultaneously
# Input 0: delay=5, Input 1: delay=3, Input 2: delay=1, Input 3: delay=0
for i, delay in enumerate([5, 3, 1, 0]):
# Connect individual input neuron to detector
src_slice = inputs[i]
# Use a separate connection for each delay value
net.connect(inputs, detector, topology="one_to_one",
weight=300, delay=delay) if i == 0 else None
net2 = nc.Network()
i0 = net2.population(1, params={"threshold": 100, "leak": 0, "refrac": 10}, label="in0")
i1 = net2.population(1, params={"threshold": 100, "leak": 0, "refrac": 10}, label="in1")
i2 = net2.population(1, params={"threshold": 100, "leak": 0, "refrac": 10}, label="in2")
i3 = net2.population(1, params={"threshold": 100, "leak": 0, "refrac": 10}, label="in3")
det = net2.population(1, params={"threshold": 800, "leak": 50, "refrac": 3},
label="detector")
# Different delays: if inputs fire at the same time, arrivals stagger
# If inputs fire in sequence (i0@t0, i1@t2, i2@t4, i3@t5),
# with delays (5,3,1,0), all arrive at t=5 -> coincidence!
net2.connect(i0, det, topology="all_to_all", weight=300, delay=5)
net2.connect(i1, det, topology="all_to_all", weight=300, delay=3)
net2.connect(i2, det, topology="all_to_all", weight=300, delay=1)
net2.connect(i3, det, topology="all_to_all", weight=300, delay=0)
sim = nc.Simulator()
sim.deploy(net2)
# Test 1: Staggered inputs that arrive simultaneously at detector
print("\nTest 1: Temporally coded pattern (inputs staggered to coincide)")
sim.inject(i0, current=200) # fires at t=0
sim.run(2)
sim.inject(i1, current=200) # fires at t=2
sim.run(2)
sim.inject(i2, current=200) # fires at t=4
sim.run(1)
sim.inject(i3, current=200) # fires at t=5
result = sim.run(10)
p = result.placement
det_gid = p.neuron_map[(det.id, 0)][0] * NEURONS_PER_CORE + p.neuron_map[(det.id, 0)][1]
det_spikes = result.spike_trains.get(det_gid, [])
print(f" Detector spikes: {len(det_spikes)} (expect >= 1 from coincidence)")
# Test 2: Simultaneous inputs (arrive at different times -> no coincidence)
sim2 = nc.Simulator()
sim2.deploy(net2)
print("\nTest 2: Simultaneous inputs (delays spread arrivals)")
sim2.inject(i0, current=200)
sim2.inject(i1, current=200)
sim2.inject(i2, current=200)
sim2.inject(i3, current=200)
result2 = sim2.run(15)
det_spikes2 = result2.spike_trains.get(det_gid, [])
print(f" Detector spikes: {len(det_spikes2)} (spread arrivals, may or may not fire)")
# Summary
print(f"\nNetwork: {net2.total_neurons()} neurons, "
f"4 delay connections (0,1,3,5 timesteps)")
print("Done!")
if __name__ == "__main__":
main()
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