fix(SNNBot): enforce sparse reservoir state via k-Winners-Take-All

Dense firing (~50%) caused all readout bins to saturate identically.
Replace threshold-based firing with k-WTA: only top 50 neurons fire
per tick (~5% sparsity). Sparse patterns give low inter-angle overlap
-> readout bins can discriminate between different bearings.

SNNBot_garage/src/reservoir.nim

@@ -1,6 +1,6 @@
 # ponytail: binary reservoir aimer — prototype; if reservoir projection is poor, scale RESERVOIR_SIZE to 4096
 
-import std/[bitops, math]
+import std/[algorithm, bitops, math]
 
 # ── Constants ──────────────────────────────────────────────────────────────────
 
@@ -11,6 +11,8 @@ const
   INPUT_BITS*     = 80
   SPARSITY_IN  = 0.1
   SPARSITY_REC = 0.05
+  K_ACTIVE     = 50  # ~5% of RESERVOIR_SIZE fire per tick
+  # ponytail: K_ACTIVE=50 gives ~5% sparsity; increase if readout can't discriminate, decrease if patterns overlap too much
 
 # ── Types ──────────────────────────────────────────────────────────────────────
 
@@ -21,7 +23,6 @@ type
   Reservoir* = object
     wIn:      array[RESERVOIR_SIZE, BitVec80]
     wRec:     array[RESERVOIR_SIZE, BitVec]
-    threshold: array[RESERVOIR_SIZE, int]
     state:    BitVec
     readout:  array[N_BINS, BitVec]
     scores*:  array[N_BINS, int]
@@ -61,27 +62,33 @@ proc initReservoir*(seed: int): Reservoir =
     for w in 0 ..< 16:
       result.wRec[i][w] = sparseBits(result, SPARSITY_REC)
 
-    # ponytail: threshold=1 gives ~50% firing; raise if reservoir saturates (all neurons fire every tick)
-    result.threshold[i] = 1
-
   # readout and state are zero-initialized by default
 
 # ── Forward ────────────────────────────────────────────────────────────────────
 
 proc forward*(r: var Reservoir, input: BitVec80): int =
-  var newState: BitVec
-
+  # Compute activation scores for all neurons
+  var activations: array[RESERVOIR_SIZE, int]
   for i in 0 ..< RESERVOIR_SIZE:
-    let inScore  = popcount(input[0] and r.wIn[i][0]) +
-                   popcount(input[1] and r.wIn[i][1])
+    let inScore  = popcount(input[0] and r.wIn[i][0]).int +
+                   popcount(input[1] and r.wIn[i][1]).int
     var recScore = 0
     for w in 0 ..< 16:
-      recScore += popcount(r.state[w] and r.wRec[i][w])
+      recScore += popcount(r.state[w] and r.wRec[i][w]).int
+    activations[i] = inScore + recScore
 
-    if inScore + recScore > r.threshold[i]:
-      let wordIdx = i shr 6   # i div 64
-      let bitIdx  = i and 63  # i mod 64
-      newState[wordIdx] = newState[wordIdx] or (1'u64 shl bitIdx)
+  # k-WTA: find K-th highest activation via sort on a copy
+  var sorted = activations
+  sort(sorted, order = SortOrder.Descending)
+  let kThreshold = sorted[min(K_ACTIVE - 1, RESERVOIR_SIZE - 1)]
+
+  # Fire exactly K_ACTIVE neurons (tie-break: first in index order)
+  var newState: BitVec
+  var count = 0
+  for i in 0 ..< RESERVOIR_SIZE:
+    if activations[i] >= kThreshold and count < K_ACTIVE:
+      newState[i shr 6] = newState[i shr 6] or (1'u64 shl (i and 63))
+      inc count
 
   r.state = newState