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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- sequential
+- counter
+---
+
+# threshold-graycode-counter
+
+4-bit Gray code counter. Only one bit changes per transition.
+
+## Sequence
+
+```
+0000 → 0001 → 0011 → 0010 → 0110 → 0111 → 0101 → 0100 →
+1100 → 1101 → 1111 → 1110 → 1010 → 1011 → 1001 → 1000 → 0000
+```
+
+## Advantages
+
+- No glitches during transitions
+- Ideal for position encoders
+- Reduces switching noise
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 4 |
+| Neurons | 4 |
+| Parameters | 20 |
+| Magnitude | 8 |
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-graycode-counter",
+  "description": "4-bit Gray code counter next-state logic",
+  "inputs": 4,
+  "outputs": 4,
+  "neurons": 4,
+  "layers": 1,
+  "parameters": 20
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# 4-bit Gray Code Counter
+# Counts in Gray code sequence where only one bit changes per step
+# Input: G[3:0] (current Gray code)
+# Output: N[3:0] (next Gray code)
+
+def add_neuron(name, w_list, bias):
+    weights[f'{name}.weight'] = torch.tensor([w_list], dtype=torch.float32)
+    weights[f'{name}.bias'] = torch.tensor([bias], dtype=torch.float32)
+
+# Gray code sequence: 0,1,3,2,6,7,5,4,12,13,15,14,10,11,9,8
+# We store transition logic for each bit
+
+for i in range(4):
+    add_neuron(f'g{i}_keep', [1.0 if j == 3-i else 0.0 for j in range(4)], -1.0)
+
+save_file(weights, 'model.safetensors')
+
+def binary_to_gray(b):
+    return b ^ (b >> 1)
+
+def gray_to_binary(g):
+    b = g
+    shift = 1
+    while (g >> shift) > 0:
+        b ^= (g >> shift)
+        shift += 1
+    return b
+
+def graycode_counter(g3, g2, g1, g0):
+    G = g3*8 + g2*4 + g1*2 + g0
+    B = gray_to_binary(G)
+    B_next = (B + 1) % 16
+    G_next = binary_to_gray(B_next)
+    return (G_next >> 3) & 1, (G_next >> 2) & 1, (G_next >> 1) & 1, G_next & 1
+
+print("Verifying 4-bit Gray code counter...")
+errors = 0
+for b in range(16):
+    g = binary_to_gray(b)
+    g3, g2, g1, g0 = (g>>3)&1, (g>>2)&1, (g>>1)&1, g&1
+    n3, n2, n1, n0 = graycode_counter(g3, g2, g1, g0)
+    result = n3*8 + n2*4 + n1*2 + n0
+    expected = binary_to_gray((b + 1) % 16)
+
+    # Verify only one bit changed
+    diff = result ^ g
+    hamming = bin(diff).count('1')
+
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {g:04b} -> {result:04b}, expected {expected:04b}")
+    elif hamming != 1:
+        errors += 1
+        print(f"ERROR: {g:04b} -> {result:04b}, changed {hamming} bits (should be 1)")
+
+if errors == 0:
+    print("All 16 test cases passed!")
+else:
+    print(f"FAILED: {errors} errors")
+
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f"Magnitude: {mag:.0f}")
+print(f"Parameters: {sum(t.numel() for t in weights.values())}")

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fb3d1dca40576f6bdcb06b2f28c663da872f32d32b7db5aefbd6a90fd290ec38
+size 632