feat: model endogenous mixed apical traffic

1 files changed, 10 insertions, 1 deletions

diff --git a/experiments/run.py b/experiments/run.py
index b412b04..cc3e475 100644
--- a/experiments/run.py
+++ b/experiments/run.py
@@ -65,7 +65,8 @@ def build(args, device):
     net = SDILNet(sizes, act=args.act, device=device, seed=args.seed,
                   w_scale=args.w_scale, a_scale=args.a_scale,
                   nuis_rho=args.nuis_rho, feedback=args.feedback,
-                  residual=bool(args.residual), predictor_mode=args.predictor_mode)
+                  residual=bool(args.residual), predictor_mode=args.predictor_mode,
+                  traffic_mode=args.traffic_mode)
     if args.mode == "dfa":
         cfg = dfa_config(eta=args.eta, momentum=args.momentum)
     elif args.mode == "sdil":
@@ -169,6 +170,9 @@ def train(args):
                     rec["cos_apical_negg"] = al["cos_apical_negg"]
                     rec["cos_Ac_negg"] = al["cos_Ac_negg"]
                     rec["r_norm"] = al["r_norm"]
+                    rec["traffic_norm"] = al["traffic_norm"]
+                    rec["traffic_residual_norm"] = al["traffic_residual_norm"]
+                    rec["traffic_r2"] = al["traffic_r2"]
                     if args.mode == "sdil" and step % (args.log_every * 5) == 0:
                         rec["ldr"] = probes.loss_decrease_ratio(net, px, py, poh, cfg, step)
                 log["steps"].append(rec)
@@ -201,6 +205,9 @@ def train(args):
         log["final"]["cos_innovation_negg"] = al["cos_innovation_negg"]
         log["final"]["cos_apical_negg"] = al["cos_apical_negg"]
         log["final"]["cos_Ac_negg"] = al["cos_Ac_negg"]
+        log["final"]["traffic_norm"] = al["traffic_norm"]
+        log["final"]["traffic_residual_norm"] = al["traffic_residual_norm"]
+        log["final"]["traffic_r2"] = al["traffic_r2"]
     os.makedirs(args.outdir, exist_ok=True)
     outpath = os.path.join(args.outdir, f"{args.tag}.json")
     with open(outpath, "w") as f:
@@ -252,6 +259,8 @@ def get_args():
     p.add_argument("--p_warmup_steps", type=int, default=200)  # pre-task neutral P warmup
     p.add_argument("--p_warmup_eta", type=float, default=0.05)
     p.add_argument("--nuis_rho", type=float, default=0.0)
+    p.add_argument("--traffic_mode", default="soma",
+                   choices=["none", "soma", "topdown", "mixed"])
     p.add_argument("--predictor_mode", default="diagonal", choices=["diagonal", "full"])
     p.add_argument("--normalize_delta", type=int, default=0)
     p.add_argument("--settle_steps", type=int, default=0)