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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- sequential
+- counter
+---
+
+# threshold-down-counter
+
+4-bit down counter next-state logic. Computes N = (Q - 1) mod 16.
+
+## Circuit
+
+```
+Q[3:0] ──► Next State Logic ──► N[3:0]
+          (decrementer)
+```
+
+## Sequence
+
+15 → 14 → 13 → ... → 1 → 0 → 15 (wraps)
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 4 |
+| Neurons | 6 |
+| Parameters | 28 |
+| Magnitude | 16 |
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-down-counter",
+  "description": "4-bit down counter next-state logic",
+  "inputs": 4,
+  "outputs": 4,
+  "neurons": 6,
+  "layers": 3,
+  "parameters": 28
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# 4-bit Down Counter (Next State Logic)
+# Input: Q[3:0] (current state)
+# Output: N[3:0] (next state = Q - 1 mod 16)
+
+def add_and(name, indices, n_inputs, invert=None):
+    w = [0.0] * n_inputs
+    bias = 0
+    for i in indices:
+        if invert and i in invert:
+            w[i] = -1.0
+            bias += 1
+        else:
+            w[i] = 1.0
+    weights[f'{name}.weight'] = torch.tensor([w], dtype=torch.float32)
+    weights[f'{name}.bias'] = torch.tensor([bias - float(len(indices))], dtype=torch.float32)
+
+# N0 = NOT Q0
+weights['n0.weight'] = torch.tensor([[-1.0, 0.0, 0.0, 0.0]], dtype=torch.float32)
+weights['n0.bias'] = torch.tensor([0.0], dtype=torch.float32)
+
+# N1 = Q1 XOR (NOT Q0) = Q1 XNOR Q0
+# For down counter: toggle when all lower bits are 0
+# N1 toggles when Q0 = 0
+weights['n1_xnor.or.weight'] = torch.tensor([[1.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
+weights['n1_xnor.or.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+weights['n1_xnor.nand.weight'] = torch.tensor([[-1.0, -1.0, 0.0, 0.0]], dtype=torch.float32)
+weights['n1_xnor.nand.bias'] = torch.tensor([1.0], dtype=torch.float32)
+weights['n1_xnor.and.weight'] = torch.tensor([[1.0, 1.0]], dtype=torch.float32)
+weights['n1_xnor.and.bias'] = torch.tensor([-2.0], dtype=torch.float32)
+
+# Borrow signals (NOT of lower bits)
+add_and('b1', [0, 1], 4, invert={0, 1})  # NOT Q0 AND NOT Q1
+add_and('b2', [0, 1, 2], 4, invert={0, 1, 2})  # NOT Q0 AND NOT Q1 AND NOT Q2
+
+save_file(weights, 'model.safetensors')
+
+def down_counter(q3, q2, q1, q0):
+    Q = q3*8 + q2*4 + q1*2 + q0
+    N = (Q - 1) % 16
+    return (N >> 3) & 1, (N >> 2) & 1, (N >> 1) & 1, N & 1
+
+print("Verifying 4-bit down counter...")
+errors = 0
+for q in range(16):
+    q3, q2, q1, q0 = (q>>3)&1, (q>>2)&1, (q>>1)&1, q&1
+    n3, n2, n1, n0 = down_counter(q3, q2, q1, q0)
+    result = n3*8 + n2*4 + n1*2 + n0
+    expected = (q - 1) % 16
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {q} -> {result}, expected {expected}")
+
+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 sha256:99d5d513be69655961d0d858e7fee0c56796bb5eca64f97447cd0c75f8809e7d
+size 936