diff --git a/docs/development-goal.md b/docs/development-goal.md index c6ce3a8..dabe8e6 100644 --- a/docs/development-goal.md +++ b/docs/development-goal.md @@ -7,11 +7,13 @@ real optional CUDA support, reusable demo kits and a more open architecture. The current design proposal is [RFC 0001](rfcs/0001-open-runtime-and-demo-kits.zh-CN.md). RFC signatures remain proposals unless listed in the current API guide. Implemented slices now include the open Python API, real model, JS CPU circuit -lab, standalone HTML templates and verified browser-to-Python replay. CUDA, -additional engine adapters, streaming audio and learning remain open. +lab, standalone HTML templates and verified browser-to-Python replay. Current source +also contains actual-device-tested CUDA and Godot. Additional engine adapters, +streaming audio and validated biological learning remain future work. The live browser game now shares the JS CPU core and is checked against Python feedback. Basic composable sessions, recorded dodge/sonification kits and full feedback replay -are implemented; CUDA remains a separate draft awaiting hardware validation. +are implemented. CUDA passed actual A16 validation and merged into main, while +the last tagged release (v0.4.0a4) remains CPU-only. Acceptance criteria: @@ -46,4 +48,15 @@ checkpoints. Headless conformance covers toy and real models. Native macOS run/p save/restore, file-dialog cancellation and disconnect/reset checks passed on 2026-09-11. See its README for usage and verification. -Current evidence and remaining release gates: [delivery audit](completion-audit-2026-09-10.md). +User steering also requested the full FlyWire graph on GPU in a voxel food-odor +environment. [That recorded experiment](validation/flywire-voxel-a16.md) now exists: +all 139,255 neurons and source rows, sensory input, a fixed motor readout, a silenced +control and exact joint checkpoint replay. Movement occurs but food contact does +not. A recorded negative outcome is not evidence of learned or reliable foraging. + +The [historical delivery audit](completion-audit-2026-09-10.md) predates CUDA +validation. Current evidence is in [CUDA](cuda.md), [full-brain validation](fullbrain-validation.md) +and [synaptic dynamics](synaptic-dynamics.md). The remaining delivery gate is an +integrated release with final packaging, clean-install and public-site verification. +The [odor gain pilot](odor-calibration-pilot.md) is a separately documented research +experiment, not a substitute for that release gate. diff --git a/docs/odor-calibration-pilot.md b/docs/odor-calibration-pilot.md new file mode 100644 index 0000000..d2f8169 --- /dev/null +++ b/docs/odor-calibration-pilot.md @@ -0,0 +1,84 @@ +# Full-brain odor gain pilot: protocol before results + +**Executed on A16 on 2026-09-13:** [all eight results and hardware evidence](validation/odor-gain-pilot-a16.md). +The protocol below was published in commit +`433c77f6c6da3b25c10996847b788d0bca871b4d` before execution and was not changed +in response to the results. No gain or behavioral default was selected. + +This protocol tests whether changing recurrent contact strength alters transient +side responses and post-stimulus spiking in the experimental synaptic engine. +It does **not** test banana identification, flight, learning or food seeking. +There is no environment, fitted action decoder or target position in this pilot. +The source commit should be published before the GPU run; publish every result, +including non-responsive, persistently active or failed conditions. + +## Why this experiment + +The [previous full-brain odor experiment](validation/flywire-synaptic-odor-a16.md) +showed a cumulative left DNa02 bias under either stimulus side. Its time series +also contains an early right DNa02 response to right input. The +[post-stimulus counts](validation/synaptic-post-stimulus.json) show actual new +downstream spikes during the observed recovery window. Cumulative counts alone +hide timing; an exponentially smoothed rate alone cannot prove continued firing. + +## Fixed design + +- All 139,255 FlyWire v783 neurons and all 16,847,997 source aggregate edges. + The official source files and annotation mapping must pass their pinned checksums. + No edge pruning, incoming normalization, added background drive or rescue reflex. +- Four contact strengths: **0.05, 0.10, 0.175, 0.275 mV/contact**. Excitatory and + inhibitory contacts scale together. These are exploratory values, not measured + biological parameters. All other [synaptic settings](synaptic-dynamics.md) stay fixed. +- Each strength uses both **left → right** and **right → left** input order, + making eight runs. JSON state is restored only between runs. +- Each run: 50 ms silent baseline → 150 ms first odor → 200 ms recovery → + 150 ms other-side odor → 250 ms recovery. No state reset between these phases. +- Fixed `dt = 0.1 ms`, seed `20260914`, NumPy PCG64. Both sides' random draws are + consumed at every tick, even if their input is disabled, preserving matched + per-neuron input sequences across gains and orders. Left/right populations have + different sizes and independently sampled events; they are not identical copies. +- Only the pinned 35 left and 33 right DM1 ORNs receive input. Bernoulli probability + is `150 Hz × dt / 1000`; each event supplies a 68.75 mV input jump. This jump + **does not scale with recurrent contact strength**. The physiological accuracy + of this single-channel odor adapter has not been established. + +## Prespecified measurements + +Each phase records actual per-neuron spike totals, first-50-ms counts, group totals, +and group spike counts in its last 100 ms (the initial rest is only 50 ms). +An additional 10 ms trace records smoothed mean rates for visualization. + +For each stimulus, report DNa02 **ipsilateral minus contralateral** firing rate +separately for the first 50 ms and the remaining 100 ms, plus cumulative +right-minus-left spike count. The pinned mapping contains one DNa02 cell per side. +Positive ipsilateral difference is descriptive; it is not an established action +label. Compare the same side when presented first versus second. + +For each recovery, report **new spikes** in its final 100 ms in DM1 ORNs, ALPNs, +MBONs, descending neurons and DNa02. A quiet finite window does not establish +long-term stability; persistent activity alone does not establish memory. + +Report all four strengths and both orders without choosing a winner automatically. +A candidate from this one-seed pilot requires independently specified multiple-seed +and held-out stimulus validation before changing a public behavioral preset. +Changing the gain must not be described as restoring validated biological behavior. + +## Reproduce on an actual GPU + +Install the repository with dataset support, pytest and a compatible CuPy CUDA +installation, and download the same pinned data files used by the +[full-brain odor experiment](validation/flywire-synaptic-odor-a16.md). + +```sh +python scripts/validate_synaptic_cuda.py --output results/hardware-gate.json +python scripts/calibrate_fullbrain_odor.py \ + --data-dir data --annotations data/annotations.tsv \ + --output results/odor-gain-pilot.json +FLYBRAIN_REQUIRE_CUDA=1 python -m pytest -q +``` + +The hardware gate must report five executed passing GPU cases and zero skips. +There is no CPU fallback in this experiment. Retain the full output JSON, hardware +report, test results, source hashes and dependency versions. Incomplete JSON is +marked `running` or `failed`; only all eight finished runs may be `completed`. +The ordinary CPU SDK remains usable without CUDA. diff --git a/docs/olfactory-experiment.md b/docs/olfactory-experiment.md index a5b3f98..2cc32f5 100644 --- a/docs/olfactory-experiment.md +++ b/docs/olfactory-experiment.md @@ -1,7 +1,12 @@ # Full-brain food-odor experiment -**GPU probe completed; navigation not demonstrated.** -See the [negative result and parameter diagnosis](validation/flywire-odor-a16.md). The full FlyWire graph has +**Two GPU probes completed with different dynamics; navigation not demonstrated.** +The historical dimensionless benchmark could not propagate sensory-only input; +see its [negative result and parameter diagnosis](validation/flywire-odor-a16.md). +The separate [synaptic mV probe](validation/flywire-synaptic-odor-a16.md) did produce +ALPN, MBON and descending spikes. Its [voxel recording](validation/flywire-voxel-a16.md) +shows movement but no food contact. Do not transfer a result between these presets. +The full FlyWire graph has passed the [CUDA numerical benchmark](fullbrain-validation.md). This experiment adds anatomically identified sensory input. It does not establish that the fly recognizes a banana, seeks food, or flies. No CUDA is needed to build the mapping, @@ -32,7 +37,7 @@ member participates in this particular odor response. ## Reproduce the mapping and GPU probe -Start with the CUDA development branch and the full-brain data/environment +Use a pinned repository commit containing the CUDA engines and the full-brain data/environment instructions in [fullbrain-validation.md](fullbrain-validation.md). Download the annotation file into your experiment folder, outside the repository: @@ -61,6 +66,10 @@ separate source/license metadata in the full-brain data instructions. ## Sensory and world assumptions +The following `OlfactoryDrive` adapter and `probe_fullbrain_odor.py` command describe +the **historical dimensionless engine**. They are not the mV input used in the +newer GPU recording. + `flybrain.olfaction.OlfactoryDrive` takes two local antenna concentration samples. It injects only the selected sensory neurons. It receives no banana coordinates, target bearing, desired turn or reward. Its stateless response is @@ -71,15 +80,30 @@ channels can be added explicitly by creating additional adapters and summing their vectors. There is intentionally no scientifically unqualified `banana` preset. +For the **synaptic mV voxel experiment**, each antenna's dimensionless local sample +is instead mapped to `rate_hz = 180 * concentration / (0.2 + concentration)`. +At every 0.1 ms tick, each selected ORN independently receives an input event with +probability `rate_hz * 0.1 / 1000`. Each event adds 68.75 mV to that input cell's +membrane voltage under the [engine's documented update order](synaptic-dynamics.md). +The 150 Hz fixed-input odor probe uses the same event mechanism. These rates and +voltage jumps approximate receptor transduction; they are not measured banana +dose-response curves. The generated events enter only the 68 selected ORNs, +while all 139,255 neurons and source edges participate in the simulation. + `sample_odor(antenna_xyz, source_xyz)` is a game-side isotropic Gaussian field. Sample it separately at each antenna. It models neither turbulent plumes nor obstacle-induced airflow. The body/world owns spatial coordinates; the neural -controller receives concentrations only. The initial world will be a kinematic +controller receives concentrations only. The recorded world uses a kinematic body in a voxel scene, not a reconstruction of flight musculature or a full ventral nerve cord. FlyWire v783 is a whole-brain dataset, not the whole animal's nervous system. -## Falsifiable first gate +## Falsifiable first gate and next calibration + +The five-condition gate below was run for both engines. The +[prespecified gain pilot](odor-calibration-pilot.md) now asks how recurrent strength +affects transient side responses, later bias and recovery, before selecting a +behavioral preset. Its design is separate from the completed five-condition results. Five conditions reset to the identical resting checkpoint: no odor, left odor, right odor, bilateral odor, and bilateral odor with the 68 ORNs silenced. Each diff --git a/docs/roadmap.md b/docs/roadmap.md index ca79b7d..32033e6 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -1,5 +1,23 @@ # Roadmap +## Verified in current source; next release pending + +- Optional CUDA backend passed actual NVIDIA A16 conformance checks. CPU remains + the default and does not require CuPy. See [CUDA evidence](cuda.md). +- Full FlyWire v783 numerical benchmark: all 139,255 neurons and source edges, + CPU/GPU comparison, checkpoint replay and stability measurement. Not real time. +- Separate synaptic mV engine matches an independent Brian2 oracle and actual + GPU tests. Sensory-only input reaches downstream groups; biological behavior + is not established. See [synaptic dynamics](synaptic-dynamics.md). +- Full-brain GPU voxel recording with an input-silenced control and paired + brain/world/input-RNG replay. The untrained readout moves but does not reach food. +- [Odor gain pilot protocol](odor-calibration-pilot.md) separates transient side + responses from persistent activity before further behavioral calibration. + +The released v0.4.0a4 remains CPU-only. An integrated CUDA release still requires +packaging and clean-install verification at its final release commit. Raw full-brain +loading remains a research-script workflow, not a `FlyBrain.load()` catalog entry. + ## Available in 0.4.0a4 - Actual Godot 4 CPU scene, offline toy and optional real MaleCNS model. @@ -29,7 +47,7 @@ - [Integration guide](demo-kits.md) and a gallery with a silenced-output comparison. Still open: additional engine adapters, streaming audio and learned readouts. -CUDA implementation is in a separate draft PR pending actual-device validation. +CUDA subsequently passed actual-device validation; see the current-source section above. ## Available in 0.3.0a1 @@ -38,8 +56,8 @@ CUDA implementation is in a separate draft PR pending actual-device validation. - Standalone editable HTML export and browser command recordings replayed in Python. - [Browser guide](browser-lab.md) with exact semantics and current limitations. -Next: optional CUDA tested on hardware, additional game adapters, then audio and -rhythm demo kits sharing the open core. Browser WASM remains a separate backend. +Later versions added Godot and audio demo kits; current source includes hardware-tested +CUDA. Browser WASM remains a separate, unimplemented backend. ## Available in 0.2.0a1 @@ -49,7 +67,8 @@ rhythm demo kits sharing the open core. Browser WASM remains a separate backend. - The previous alpha API and schema-1 checkpoint reader remain supported. The circuit lab shipped in 0.3; basic game/audio sessions, the live game and Godot -adapter shipped in 0.4. Streaming audio, trained rhythm templates and validated CUDA remain open. See [RFC 0001](rfcs/0001-open-runtime-and-demo-kits.zh-CN.md) for the +adapter shipped in 0.4. Streaming audio and trained rhythm templates remain open. +CUDA has since passed hardware checks. See [RFC 0001](rfcs/0001-open-runtime-and-demo-kits.zh-CN.md) for the proposed architecture; proposal-only APIs are not current API documentation. @@ -73,7 +92,7 @@ proposed architecture; proposal-only APIs are not current API documentation. - Extend Godot, add Unity and richer game examples. - Compact sparse-array model/checkpoint format for large graphs. - WASM reference implementation and TypeScript package. -- Optional CUDA backend, only after cross-backend conformance tests. +- Publish the integrated optional CUDA release after clean-install checks. - Explore learning/plasticity separately from the fixed-connectome MVP. Open issues and propose focused milestones; these are directions, not release-date promises. diff --git a/docs/synaptic-dynamics.md b/docs/synaptic-dynamics.md index d8cf5db..72c2540 100644 --- a/docs/synaptic-dynamics.md +++ b/docs/synaptic-dynamics.md @@ -11,6 +11,10 @@ it is not yet exposed by `FlyBrain.load()` or a model catalog entry. It requires explicit millivolt weights and rejects dimensionless weight units. The original benchmark is retained as a reproducible historical numerical test. +The [completed odor gain pilot](validation/odor-gain-pilot-a16.md) compares four contact +strengths and both stimulus orders to separate early side responses from sustained +bias and recovery spiking. All eight GPU runs completed; no behavioral preset was selected. + ## Model and source Nominal equations and parameters follow [Shiu et al., Nature (2024)](https://www.nature.com/articles/s41586-024-07763-9) diff --git a/docs/validation/odor-gain-cuda-a16.json b/docs/validation/odor-gain-cuda-a16.json new file mode 100644 index 0000000..f2aadb7 --- /dev/null +++ b/docs/validation/odor-gain-cuda-a16.json @@ -0,0 +1,25 @@ +{ + "status": "passed", + "python": "3.12.3", + "source_sha256": { + "src/flybrain/experimental/__init__.py": "570d34f551fd63b33bff564c022ad01fc463b0e1649d9f7035e361b421533bd5", + "src/flybrain/experimental/synaptic.py": "4c3b7b48e659314a1dd839819ddce4bdd1622d21dd8753400035c1df8deaa726", + "src/flybrain/experimental/synaptic_cuda.py": "8b0bc40f8bf0075e6d81caadf3393673110aa48b66415303a08020cb9055dfa8", + "src/flybrain/experimental/voxel_arena.py": "d358df33830ca4d96fe8c5c8549cef383760213d00f8e153f64e2b9c5e7484b3", + "tests/test_synaptic_cuda_hardware.py": "70761bdbb7cbd4fdaa82ea85b40494a84fb532d9d6af350449aa33e46219bb1e", + "scripts/validate_synaptic_cuda.py": "7f51e7147d8c51d879a663e85841077197196851648e780d05fb86eceaf13c80" + }, + "device": { + "name": "NVIDIA A16-8Q", + "cupy": "14.2.0", + "memory_bytes": 8394047488, + "driver": 12040, + "runtime": 12090 + }, + "cases": { + "tests": 5, + "skipped": 0, + "errors": 0, + "failures": 0 + } +} diff --git a/docs/validation/odor-gain-gpu-active.csv b/docs/validation/odor-gain-gpu-active.csv new file mode 100644 index 0000000..c7b473c --- /dev/null +++ b/docs/validation/odor-gain-gpu-active.csv @@ -0,0 +1,2 @@ +timestamp, name, utilization.gpu [%], memory.used [MiB], memory.total [MiB] +2026/09/13 04:03:01.738, NVIDIA A16-8Q, 87 %, 289 MiB, 8192 MiB diff --git a/docs/validation/odor-gain-pilot-a16.md b/docs/validation/odor-gain-pilot-a16.md new file mode 100644 index 0000000..062af07 --- /dev/null +++ b/docs/validation/odor-gain-pilot-a16.md @@ -0,0 +1,84 @@ +# Full FlyWire odor gain pilot — NVIDIA A16 + +**All eight full-graph CUDA runs completed. This pilot did not identify a preset +that establishes reliable odor-directed navigation. No behavioral default changed.** +[Interactive comparison](https://freeman-1984-coder.github.io/flybrain-sdk/odor-calibration.html) +· [Unmodified raw report](../../site/experiments/odor-gain-pilot.json) +· [Prespecified protocol](../odor-calibration-pilot.md). + +## Execution and provenance + +- Public pre-run commit: `433c77f6c6da3b25c10996847b788d0bca871b4d`. +- Ran 2026-09-13 03:49:16–04:07:47 UTC, including graph construction between gains. +- Four strengths × both stimulus orders; 800 ms per run, 0.1 ms ticks, seed 20260914. +- All 139,255 neurons, 16,847,997 source rows, 54,492,922 anatomical contacts retained + at every strength. No additional pruning or incoming normalization. Source file + hashes and a separate graph-array hash for each strength are in the raw report. +- NVIDIA A16-8Q, 8 GB virtual GPU; Python 3.12.3, CuPy 14.2.0, + driver API 12040 and CUDA runtime 12090. Full dependencies are + [recorded here](odor-gain-requirements.txt). +- [Hardware gate](odor-gain-cuda-a16.json): five GPU cases passed, zero skipped. +- [Full hardware-host test suite](odor-gain-pytest.xml): 217 passed, zero skipped. +- [69 Python source-file hashes](odor-gain-source-sha256.txt) verified against the + pre-run commit after retrieval. Group totals and onset metrics were independently + recomputed from the per-neuron counts; all eight planned conditions were checked. +- [During-run GPU observation](odor-gain-gpu-active.csv): 87% GPU utilization and + 289 MiB GPU memory at that one instant. It is not a peak-memory measurement. +- Individual 800 ms runs took 101.9–103.7 seconds, including observations. This is + not real-time simulation and is not the older dimensionless benchmark's speedup. + +The retrieved evidence archive SHA256 was +`5028ab681c2f634ceeafb2de81c78cd07f501f29c1b7f75504ca1a69c188f081`. +The unmodified published raw report SHA256 is +`e72b30c19b76efb3a7f92227519a65ee68629690f216b35dfd6fdbe7170c011f`. +The archive contains setup logs as well as the individually published evidence; +its digest is a transfer check, not a URL to an uploaded archive. + +## Prespecified results + +Onset means the first 50 ms of each 150 ms stimulus; later means its remaining +100 ms. DNa02 values are **ipsilateral minus contralateral Hz**, with one cell on +each side. Recovery values are **new descending-neuron spikes in the final 100 ms** +of each recovery, not residual smoothed rates. Raw data retains all other groups. + +| mV/contact | Order | Onset 1 Hz | Onset 2 Hz | Later 1 Hz | Later 2 Hz | Recovery 1 spikes | Recovery 2 spikes | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | +| 0.05 | left → right | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | +| 0.05 | right → left | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | +| 0.1 | left → right | 0.0 | 0.0 | 0.0 | 0.0 | 17 | 17 | +| 0.1 | right → left | 0.0 | 0.0 | 0.0 | 0.0 | 16 | 17 | +| 0.175 | left → right | 0.0 | -20.0 | 10.0 | -10.0 | 152 | 148 | +| 0.175 | right → left | 0.0 | 0.0 | -10.0 | 0.0 | 151 | 154 | +| 0.275 | left → right | 60.0 | -40.0 | 60.0 | -30.0 | 422 | 430 | +| 0.275 | right → left | 20.0 | 20.0 | -40.0 | 40.0 | 388 | 436 | + +## Interpretation and limits + +At 0.05 mV/contact, the selected sensory neurons and a few ALPNs fired, but no +MBONs or descending neurons did. Both measured recovery tails were quiet. +At 0.10, activity reached some downstream cells but neither DNa02 fired, and +new downstream spikes continued after input ended. At 0.175 and 0.275, selected +DNa02 cells fired, with no consistent ipsilateral response across both orders +and both time windows. Downstream spiking persisted into every measured recovery +tail at these strengths. + +The 0.275 run with right input first has a positive early right response (+20 Hz) +and a later left bias (-40 Hz ipsilateral difference). Right input presented second +in the other run instead has a negative early difference (-40 Hz). This describes +an observed order/timing difference; **it does not isolate a causal memory effect**. +The first and second phases use different draws from the same absolute-time random +stream as well as different preceding neural states. Multiple seeds and matched +within-stimulus event patterns would be required to separate those effects. + +The pilot preserves fixed 68.75 mV sensory jumps while changing all recurrent +contact weights together. These are assumed homogeneous LIF dynamics and a +single-channel DM1 food-odor approximation, not measured banana chemistry, +receptor dose-response curves or calibrated cell-specific physiology. It provides +no learned decoder, flight model or closed-loop foraging validation. A positive +DNa02 difference is a measurement, not a certified motor command. + +Lowering all connection weights alone did not yield a demonstrated responsive +navigation preset in these four values and this seed. Do not promote a selected +gain or reverse a body controller based on these data. Follow-up hypotheses need +independent stimulus/seed validation and declared input/readout assumptions. +The experiment code and reproduction commands remain in the prespecified protocol. diff --git a/docs/validation/odor-gain-pytest.xml b/docs/validation/odor-gain-pytest.xml new file mode 100644 index 0000000..ca80ad2 --- /dev/null +++ b/docs/validation/odor-gain-pytest.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/validation/odor-gain-requirements.txt b/docs/validation/odor-gain-requirements.txt new file mode 100644 index 0000000..de7b492 --- /dev/null +++ b/docs/validation/odor-gain-requirements.txt @@ -0,0 +1,20 @@ +cuda-pathfinder==1.8.1 +cuda-toolkit==12.9.2.0 +cupy-cuda12x==14.2.0 +# Editable install with no version control (flybrain-sdk==0.5.0.dev0) +-e /root/odor-gain/source +iniconfig==2.3.0 +numpy==2.5.3 +nvidia-cublas-cu12==12.9.2.10 +nvidia-cuda-nvrtc-cu12==12.9.86 +nvidia-cuda-runtime-cu12==12.9.79 +nvidia-cufft-cu12==11.4.1.4 +nvidia-curand-cu12==10.3.10.19 +nvidia-cusolver-cu12==11.7.5.82 +nvidia-cusparse-cu12==12.5.10.65 +nvidia-nvjitlink-cu12==12.9.86 +packaging==26.3 +pluggy==1.6.0 +pyarrow==25.0.1 +Pygments==2.21.0 +pytest==9.1.1 diff --git a/docs/validation/odor-gain-source-sha256.txt b/docs/validation/odor-gain-source-sha256.txt new file mode 100644 index 0000000..6d73ef1 --- /dev/null +++ b/docs/validation/odor-gain-source-sha256.txt @@ -0,0 +1,69 @@ +de9596a06a4de0a04303795a10ae0a6d8cbbc7705a5e60ad566b4c1bfad65971 src/flybrain/__init__.py +c125d73bf421cac3c6a8f4185ae56516db20cc91831e006c42c544c7b873dfb9 src/flybrain/__main__.py +6c94a5cc4be4916e569562041036a6e169b13b228d2bc2dffc0494cff90bd64a src/flybrain/backends/__init__.py +bb28e7aa30f3cb3b676243e6e3767242d5daa27458b5e5a96b218232aaa1de81 src/flybrain/backends/base.py +20ff6d342efe088f161b800f825175b77b754e42378e7879e6ff5007da5fe11a src/flybrain/backends/cpu.py +da6f944b0075733d668615ec73f2822cdf740d404d06d2e8309afec1a63b9e9b 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+284e10f5db01293ad85f16d1a815e029880aaf1e3db7479917bcc7cb9ae6eefa tests/test_synaptic_odor_protocol.py +8e93c466dad65514e9dc0309d6b15764d2b16fc5e8468f15e246c509863fc31d tests/test_voxel_arena.py diff --git a/scripts/calibrate_fullbrain_odor.py b/scripts/calibrate_fullbrain_odor.py new file mode 100644 index 0000000..0bb18b9 --- /dev/null +++ b/scripts/calibrate_fullbrain_odor.py @@ -0,0 +1,284 @@ +"""Prespecified full-graph CUDA pilot: recurrent gain, side changes, recovery. + +Four contact scales and both side orders are always reported. No food world, +decoder fitting, pruning or successful-run selection. This one-seed pilot can +identify candidates for later independent validation, not validate physiology. +""" + +import argparse +import gc +import json +import time +from pathlib import Path + +import numpy as np +from build_olfactory_map import build +from probe_synaptic_odor import input_events +from validate_fullbrain import digest, load_fullbrain + +from flybrain.config import finite_number +from flybrain.experimental.synaptic import SynapticLIFConfig +from flybrain.experimental.synaptic_cuda import SynapticCUDA +from flybrain.neurons import NeuronIndex + +CONTACT_SCALES_MV = (0.05, 0.10, 0.175, 0.275) +ORDERS = (("left", "right"), ("right", "left")) +PHASES = ( + ("rest", 50), + ("first", 150), + ("recovery_first", 200), + ("second", 150), + ("recovery_second", 250), +) + + +def phase_schedule(dt_ms, order): + dt_ms = finite_number(dt_ms, "dt_ms") + if dt_ms <= 0: + raise ValueError("dt_ms must be positive") + if tuple(order) not in ORDERS: + raise ValueError("expected both left/right orders") + tick = 0 + result = [] + for name, ms in PHASES: + steps = round(ms / dt_ms) + if steps <= 0 or not np.isclose(steps * dt_ms, ms, atol=1e-12, rtol=0): + raise ValueError("phase durations must be exact multiples of dt_ms") + side = order[0] if name == "first" else order[1] if name == "second" else None + result.append( + { + "name": name, + "side": side, + "start_tick": tick, + "end_tick": tick + steps, + "duration_ms": ms, + } + ) + tick += steps + return result + + +def summarize_run(run): + """Separate transient side responses from cumulative bias and new tail spikes. + + DNa02 has exactly one annotated cell on each side in this pinned mapping. + Recovery counts are observed spikes, never the exponentially smoothed rate. + These descriptive metrics do not select a gain or certify navigation. + """ + summary = {"stimuli": [], "recovery": []} + for phase in run["phases"]: + if phase["side"] is not None: + side = phase["side"] + other = "right" if side == "left" else "left" + onset = phase["first_50ms_spikes"] + total = phase["spikes"] + ipsi, contra = f"DNa02_{side}", f"DNa02_{other}" + later_ms = phase["duration_ms"] - 50 + summary["stimuli"].append( + { + "phase": phase["name"], + "side": side, + "onset_ipsilateral_minus_contralateral_hz": (onset[ipsi] - onset[contra]) + * 1000 + / 50, + "later_ipsilateral_minus_contralateral_hz": ( + (total[ipsi] - onset[ipsi]) - (total[contra] - onset[contra]) + ) + * 1000 + / later_ms, + "total_right_minus_left_spikes": total["DNa02_right"] - total["DNa02_left"], + } + ) + elif phase["name"].startswith("recovery"): + summary["recovery"].append( + { + "phase": phase["name"], + "tail_window_ms": phase["tail_window_ms"], + "new_tail_spikes": phase["last_100ms_spikes"], + } + ) + return summary + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--data-dir", type=Path, required=True) + parser.add_argument("--annotations", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + config, seed = SynapticLIFConfig(), 20260914 + root = Path(__file__).resolve().parents[1] + report = { + "schema": "flybrain-odor-gain-pilot-v1", + "status": "running", + "started_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "config": config.to_dict(), + "numpy": np.__version__, + "source_sha256": { + str(p.relative_to(root)): digest(p) + for p in ( + Path(__file__).resolve(), + root / "scripts/build_olfactory_map.py", + root / "scripts/probe_synaptic_odor.py", + root / "scripts/validate_fullbrain.py", + *sorted((root / "src/flybrain/experimental").glob("*.py")), + ) + }, + "protocol": { + "contact_scales_mv": list(CONTACT_SCALES_MV), + "orders": ORDERS, + "seed": seed, + "rng": "NumPy PCG64", + "input_rate_hz": 150, + "input_jump_mv": 68.75, + "background_input": 0, + "sensory_model": "Bernoulli p=rate*dt; DM1 single-channel approximation", + "input_jump_independent_of_contact_scale": True, + "graph_policy": "every official neuron and aggregate edge retained", + "state_policy": "reset only between runs; never reset between phases", + "scope": "one-seed pilot; no fitted decoder, behavior or biological validation", + "schedule": phase_schedule(config.dt_ms, ORDERS[0]), + "analysis": ( + "onset 50 ms and later ipsi-minus-contra DNa02 Hz; new recovery tail spikes" + ), + "selection_policy": "report all eight runs; no automatic gain or decoder selection", + }, + "models": [], + "runs": [], + } + args.output.parent.mkdir(parents=True, exist_ok=True) + + def save(): + temp = args.output.with_suffix(".partial.json") + temp.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + temp.replace(args.output) + + save() + try: + mapping = build(args.annotations, args.data_dir / "proofread_root_ids_783.npy") + report["mapping"] = mapping + report["mapping_input_units_note"] = ( + "Protocol defines actual mV inputs, not old map labels." + ) + input_ids = mapping["groups"]["ORN_DM1_left"] + mapping["groups"]["ORN_DM1_right"] + left_n, right_n = (len(mapping["groups"][f"ORN_DM1_{s}"]) for s in ("left", "right")) + for scale in CONTACT_SCALES_MV: + model, provenance = load_fullbrain(args.data_dir, contact_mv=scale) + report["models"].append(provenance) + index = NeuronIndex(model) + inputs = np.asarray(index.resolve(input_ids), dtype=np.int64) + groups = {k: index.resolve(v) for k, v in mapping["groups"].items()} + selected = tuple(sorted({i for values in groups.values() for i in values})) + offsets = {i: j for j, i in enumerate(selected)} + positions = {k: [offsets[i] for i in values] for k, values in groups.items()} + brain = SynapticCUDA(model, config, input_ids=input_ids, weight_units="mV") + cp = brain._cp + props = cp.cuda.runtime.getDeviceProperties(brain._device.id) + report["device"] = { + "name": props["name"].decode(), + "memory_bytes": props["totalGlobalMem"], + "cupy": cp.__version__, + "driver": cp.cuda.runtime.driverGetVersion(), + "runtime": cp.cuda.runtime.runtimeGetVersion(), + } + rest = json.loads(json.dumps(brain.snapshot(), allow_nan=False)) + jumps = np.zeros(brain.n) + for order in ORDERS: + brain.restore(rest) + rng = np.random.default_rng(seed) + run = { + "contact_mv": scale, + "order": list(order), + "graph_sha256": provenance["graph_arrays_sha256"], + "selected_neuron_ids": [model.neuron_ids[i] for i in selected], + "phases": [], + "trace": [], + } + started = time.perf_counter() + for phase in phase_schedule(config.dt_ms, order): + totals = np.zeros(len(selected), dtype=np.int64) + onset = np.zeros(len(selected), dtype=np.int64) + tail = np.zeros(len(selected), dtype=np.int64) + input_count = np.zeros(2, dtype=np.int64) + for tick in range(phase["start_tick"], phase["end_tick"]): + events = input_events( + rng, + left_n, + right_n, + 150 * config.dt_ms / 1000, + phase["side"] == "left", + phase["side"] == "right", + ) + jumps[inputs] = events * 68.75 + input_count += [int(events[:left_n].sum()), int(events[left_n:].sum())] + brain.step(jumps) + spikes = np.asarray( + brain.observe_selected(selected, ("spikes",))["spikes"], dtype=np.int64 + ) + totals += spikes + if tick - phase["start_tick"] < round(50 / config.dt_ms): + onset += spikes + if tick >= phase["end_tick"] - round(100 / config.dt_ms): + tail += spikes + if (tick + 1) % 100 == 0: + rates = np.asarray( + brain.observe_selected(selected, ("rates_hz",))["rates_hz"] + ) + run["trace"].append( + { + "time_ms": (tick + 1) * config.dt_ms, + "phase": phase["name"], + "mean_rates_hz": { + k: float(rates[pos].mean()) for k, pos in positions.items() + }, + } + ) + item = { + **phase, + "input_events": input_count.tolist(), + "spikes": {k: int(totals[pos].sum()) for k, pos in positions.items()}, + "first_50ms_spikes": { + k: int(onset[pos].sum()) for k, pos in positions.items() + }, + "last_100ms_spikes": { + k: int(tail[pos].sum()) for k, pos in positions.items() + }, + "tail_window_ms": min(100, phase["duration_ms"]), + "per_neuron_spikes": totals.tolist(), + "first_50ms_per_neuron_spikes": onset.tolist(), + } + if phase["name"] == "rest" and totals.any(): + raise AssertionError("fresh no-input baseline must remain silent") + run["phases"].append(item) + run["wall_seconds"] = time.perf_counter() - started + run["summary"] = summarize_run(run) + report["runs"].append(run) + save() + print( + json.dumps( + { + "contact_mv": scale, + "order": order, + "wall_seconds": run["wall_seconds"], + "spikes": {p["name"]: p["spikes"] for p in run["phases"]}, + } + ), + flush=True, + ) + brain._stream.synchronize() + del brain, model, rest, index + gc.collect() + cp.get_default_memory_pool().free_all_blocks() + if len(report["runs"]) != 8: + raise AssertionError("all eight prespecified runs must complete") + report["status"] = "completed" + except Exception as error: + report.update(status="failed", error=f"{type(error).__name__}: {error}") + raise + finally: + report["finished_utc"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + save() + + +if __name__ == "__main__": + main() diff --git a/site/experiments/odor-gain-pilot.json b/site/experiments/odor-gain-pilot.json new file mode 100644 index 0000000..1c43d50 --- /dev/null +++ b/site/experiments/odor-gain-pilot.json @@ -0,0 +1,202348 @@ +{ + "schema": "flybrain-odor-gain-pilot-v1", + "status": "completed", + "started_utc": "2026-09-13T03:49:16Z", + "config": { + "dt_ms": 0.1, + "rest_mv": -52.0, + "reset_mv": -52.0, + "threshold_mv": -45.0, + "membrane_tau_ms": 20.0, + "synapse_tau_ms": 5.0, + "refractory_ms": 2.2, + "delay_ms": 1.8, + "rate_tau_ms": 50.0 + }, + "numpy": "2.5.3", + "source_sha256": { + "scripts/calibrate_fullbrain_odor.py": "c817fa6fbe1a4c1faeb96648353e300f5be8a6b7e494c07c2c06b92c0cb6d190", + "scripts/build_olfactory_map.py": "00c87876ce4dd1475f225333ea5776b33f1473da6d98157f21f3fd3f98efa8ae", + "scripts/probe_synaptic_odor.py": "393ae10e825cff56b58cd80f2ab8e559b55cec600886ae85131427545d1cffdf", + "scripts/validate_fullbrain.py": "4d543ed43e38cbed609d0cd54003f76049347844d7fa8af58d1ac25753157370", + "src/flybrain/experimental/__init__.py": "570d34f551fd63b33bff564c022ad01fc463b0e1649d9f7035e361b421533bd5", + "src/flybrain/experimental/synaptic.py": "4c3b7b48e659314a1dd839819ddce4bdd1622d21dd8753400035c1df8deaa726", + "src/flybrain/experimental/synaptic_cuda.py": "8b0bc40f8bf0075e6d81caadf3393673110aa48b66415303a08020cb9055dfa8", + "src/flybrain/experimental/voxel_arena.py": "d358df33830ca4d96fe8c5c8549cef383760213d00f8e153f64e2b9c5e7484b3" + }, + "protocol": { + "contact_scales_mv": [ + 0.05, + 0.1, + 0.175, + 0.275 + ], + "orders": [ + [ + "left", + "right" + ], + [ + "right", + "left" + ] + ], + "seed": 20260914, + "rng": "NumPy PCG64", + "input_rate_hz": 150, + "input_jump_mv": 68.75, + "background_input": 0, + "sensory_model": "Bernoulli p=rate*dt; DM1 single-channel approximation", + 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dff590c..a918c99 100644 --- a/site/foraging.html +++ b/site/foraging.html @@ -21,7 +21,13 @@

把完整脑，放进一个小世界。

使用下方实验摘要和 JSON 记录查看结果。

正在读取实际 GPU 记录……

0 / 2000 ms
绿色：实际轨迹黄色：食物气味源拖动场景：旋转观察角度
-

这一帧，脑子输出了什么？

等待实际记录。

如何读这个实验

比较正常神经输入和关闭嗅觉入口的同初始状态对照。轨迹来自实际记录；网页不会让角色自动朝向香蕉。

结果以记录为准，包括没有接近或接触食物的情况。

+

这一帧，脑子输出了什么？

等待实际记录。

如何读这个实验

比较正常神经输入和关闭嗅觉入口的同初始状态对照。轨迹来自实际记录；网页不会让角色自动朝向香蕉。

结果以记录为准，包括没有接近或接触食物的情况。

比较四档突触强度下的气味响应与恢复 →

哪些是连接组，哪些是我们的假设？

全部脑神经元和连接来自 FlyWire v783。输入只覆盖一个 DM1 食物气味通道，不代表完整香蕉气味或视觉识别。脑模型采用简化的突触 LIF 动力学。

动作层是未经训练的工程映射：DNa02 两侧放电频率之差控制转向、频率之和控制速度。尤其是速度映射没有生物学验证。身体是地面运动模型，没有飞行、肌肉或六足步态仿真。记录中的移动不等于可靠觅食。

下载全部帧与参数 · 复现方法与限制 · 开源仓库

+

怎么知道该刺激哪些嗅觉神经元？

+

香蕉图标在这里标记气味源的位置。我们没有让模型识别画面中的香蕉，也还没有重建香蕉的多种挥发物混合气味。

+

当前输入依据是：乙酸乙酯可以激活 Or42b 嗅觉通路，相关神经元投射到 DM1。我们根据固定版本的 FlyWire 细胞注释，选择左侧 35 个、右侧 33 个 ORN_DM1 神经元；它们只是入口，全脑的 139,255 个神经元仍参与计算。

+

环境分别计算左右触角处的局部浓度，转换成输入脉冲概率，再给对应输入细胞施加膜电位跳变。浓度与脉冲频率的关系是未经实测校准的工程近似。脑子和动作读出没有直接收到香蕉的位置或目标方向。

+

Sensory provenance: ethyl-acetate-responsive Or42b/DM1 input, 68 anatomically annotated ORNs within the full FlyWire graph. The concentration-to-spike adapter is an explicit approximation, not a validated banana mixture or receptor dose-response model.

+

受体响应实验 · 香蕉挥发物研究 · 输入映射、单位与复现方法

diff --git a/site/js/odor-calibration.js b/site/js/odor-calibration.js new file mode 100644 index 0000000..53b5ec7 --- /dev/null +++ b/site/js/odor-calibration.js @@ -0,0 +1,105 @@ +/* Recorded evidence only. Never synthesize data when the GPU report is missing. */ +const $ = (id) => document.getElementById(id); +const sideName = (side) => side === 'left' ? '左' : '右'; +const label = (run) => `${run.contact_mv} mV · ${run.order.map(sideName).join(' → ')}`; +const rateGroups = ['DNa02_left', 'DNa02_right', 'ALPN', 'MBON', 'descending']; +let report; +function row(parent, values) { + const tr = document.createElement('tr'); + values.forEach((value) => { + const td = document.createElement('td'); + td.textContent = String(value); + tr.append(td); + }); + parent.append(tr); +} +function svgNode(name, attributes, text) { + const el = document.createElementNS('http://www.w3.org/2000/svg', name); + Object.entries(attributes).forEach(([key, value]) => el.setAttribute(key, String(value))); + if (text !== undefined) el.textContent = text; + return el; +} +function render() { + if (!report) return; + const run = report.runs[Number($('run').value)]; + const population = $('population').value; + const groups = [['DNa02_left', '#93e8c2'], ['DNa02_right', '#e8b9ff'], [population, '#ffc778']]; + const chart = $('trace'); + chart.replaceChildren(svgNode('title', {}, `${label(run)}：DNa02 左右和 ${population} 平滑平均频率`)); + chart.setAttribute('aria-label', `${label(run)} 的真实 GPU 神经活动，横轴 0–800 毫秒`); + // Fixed across both conditions and population choices, so selection cannot exaggerate differences. + let maximum = 10; + for (const item of report.runs) for (const frame of item.trace) { + for (const key of rateGroups) { + maximum = Math.max(maximum, frame.mean_rates_hz[key]); + } + } + maximum = Math.ceil(maximum / 20) * 20; + const x = (ms) => 65 + ms / 800 * 910; + const y = (rate) => 320 - rate / maximum * 270; + for (const phase of run.phases.filter((p) => p.side !== null)) { + const start = phase.start_tick * report.config.dt_ms; + chart.append(svgNode('rect', {x: x(start), y: 40, width: phase.duration_ms / 800 * 910, + height: 280, fill: phase.side === 'left' ? '#93e8c2' : '#e8b9ff', opacity: 0.09})); + chart.append(svgNode('text', {x: x(start) + 8, y: 30, fill: '#aec1c2', 'font-size': 15}, `${sideName(phase.side)}侧气味`)); + } + for (let tick = 0; tick <= 4; tick++) { + const rate = maximum * tick / 4; + chart.append(svgNode('line', {x1: 65, x2: 975, y1: y(rate), y2: y(rate), stroke: '#2a3e43'})); + chart.append(svgNode('text', {x: 53, y: y(rate) + 5, fill: '#aec1c2', 'text-anchor': 'end', 'font-size': 14}, rate.toFixed(0))); + } + for (let ms = 0; ms <= 800; ms += 100) { + chart.append(svgNode('text', {x: x(ms), y: 346, fill: '#aec1c2', 'text-anchor': 'middle', 'font-size': 14}, ms)); + } + chart.append(svgNode('text', {x: 15, y: 30, fill: '#aec1c2', 'font-size': 14}, 'Hz')); + chart.append(svgNode('text', {x: 980, y: 366, fill: '#aec1c2', 'text-anchor': 'end', 'font-size': 14}, '模拟时间 / ms')); + for (const [key, color] of groups) { + chart.append(svgNode('polyline', {points: run.trace.map((f) => `${x(f.time_ms)},${y(f.mean_rates_hz[key])}`).join(' '), + fill: 'none', stroke: color, 'stroke-width': 2, + 'stroke-dasharray': key === 'DNa02_right' ? '6 4' : 'none'})); + } + $('timing').replaceChildren(); + run.summary.stimuli.forEach((stimulus) => row($('timing'), [ + sideName(stimulus.side), stimulus.onset_ipsilateral_minus_contralateral_hz, + stimulus.later_ipsilateral_minus_contralateral_hz, stimulus.total_right_minus_left_spikes, + ])); +} +async function load() { + try { + const response = await fetch('experiments/odor-gain-pilot.json'); + if (!response.ok) throw new Error(`HTTP ${response.status}`); + const data = await response.json(); + if (data.schema !== 'flybrain-odor-gain-pilot-v1' || data.status !== 'completed' || + data.runs?.length !== 8 || data.models?.length !== 4 || + data.models.some((m) => m.neurons !== 139255 || m.retained_rows !== 16847997) || + data.config?.dt_ms !== 0.1 || + data.runs.some((r, i) => r.phases?.length !== 5 || r.trace?.length !== 80 || + r.contact_mv !== [0.05, 0.10, 0.175, 0.275][Math.floor(i / 2)] || + r.order?.join(',') !== (i % 2 === 0 ? 'left,right' : 'right,left') || + r.graph_sha256 !== data.models[Math.floor(i / 2)].graph_arrays_sha256 || + r.trace.some((f, j) => Math.abs(f.time_ms - (j + 1) * 10) > 1e-6 || + rateGroups.some((key) => !Number.isFinite(f.mean_rates_hz?.[key]) || f.mean_rates_hz[key] < 0)) || + r.summary?.stimuli?.length !== 2 || r.summary?.recovery?.length !== 2)) { + throw new Error('完整八组 GPU 记录尚未通过结构检查'); + } + report = data; + $('results').replaceChildren(); + report.runs.forEach((run, index) => { + const option = document.createElement('option'); + option.value = index; option.textContent = label(run); $('run').append(option); + row($('results'), [label(run), + ...run.summary.stimuli.map((s) => s.onset_ipsilateral_minus_contralateral_hz), + ...run.summary.recovery.map((r) => r.new_tail_spikes.descending)]); + }); + $('run').disabled = false; + $('run').addEventListener('change', render); + $('population').addEventListener('change', render); + $('status').textContent = `${report.device.name} 实际运行 · 八组全部完成 · 仅为单随机种子探索，未自动选择参数。`; + $('provenance').textContent = `FlyWire v783 · 139,255 neurons · 16,847,997 edges · seed ${report.protocol.seed} · ${report.started_utc} · CuPy ${report.device.cupy}`; + render(); + } catch (error) { + $('status').textContent = `无法读取完整实验记录：${error.message}。页面不会用示意数据替代。`; + $('run').disabled = true; + } +} +load(); diff --git a/site/llms.txt b/site/llms.txt index 9a8c2ab..caed068 100644 --- a/site/llms.txt +++ b/site/llms.txt @@ -64,3 +64,5 @@ Full-brain sensory update (2026-09-13): The A16-8Q five-condition ORN_DM1 probe New synaptic-engine result (2026-09-13): The independently checked mV LIF engine passed five actual A16 GPU cases, 200 active full-graph CPU/GPU ticks and exact 20-tick checkpoint replay. Full FlyWire sensory-only input produced ALPN, MBON and descending spikes; no-odor and input-silenced controls stayed silent. DNa02 remained left-biased for both stimulus sides, so odor-directed navigation is not established. This result uses different dynamics/weights from the historical normalized benchmark. [Raw evidence and limitations](https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html#synaptic). [Full-brain GPU voxel recording](https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html): all 139,255 neurons run on actual A16 GPU; two 2-second closed-loop runs, 100 frames each. A fixed, untrained DNa02-to-speed/turn readout produces movement but curves away from food (distance 2.778 to 4.380 game units); silencing input ORNs leaves the body stationary. Neither run reaches food. 207 hardware-host software tests passed, plus exact joint brain/world/RNG next-frame replay. Recorded browser playback, not live inference, flight, biological locomotion or learned navigation. + +[Full-brain odor gain pilot](https://freeman-1984-coder.github.io/flybrain-sdk/odor-calibration.html): eight actual A16 runs compare four mV/contact strengths and both stimulus orders with all FlyWire neurons and source edges retained. At 0.05 downstream output is absent; larger strengths produce persistent downstream spikes during recovery. The 0.275 right-first run has an early ipsilateral DNa02 response but later contralateral bias. One seed, no fitted decoder, no automatic parameter selection and no navigation claim. 217 hardware-host tests passed with zero skips. The page presents all conditions, original counts and smoothed traces. [Sensory mapping explained](https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html#odor-input). diff --git a/site/odor-calibration.html b/site/odor-calibration.html new file mode 100644 index 0000000..94cc99b --- /dev/null +++ b/site/odor-calibration.html @@ -0,0 +1,34 @@ + + + + +Full-brain odor calibration · flybrain-sdk + + + + + + +
+
FLYBRAIN / ODOR CALIBRATION返回方块实验 ↗
+

气味换了方向，神经活动会跟着变吗？

+

用同一个完整 FlyWire 脑，比较四档突触强度和两种刺激顺序。重点观察刚闻到气味的响应，以及气味停止后新增的放电。所有条件都公开，避免只选看起来成功的结果。

+

Full-brain CUDA pilot: all 139,255 FlyWire neurons, four contact strengths, both stimulus orders, one fixed seed. Descriptive evidence of transient side responses and recovery; not a validation of banana recognition, navigation or biological parameters. No CUDA required to view.

+

正在读取完整实验记录……

+

本次结果：0.05 mV/contact 没有下行输出；其余三档在恢复期仍有新增的下游放电。这组单随机种子实验尚未确立可靠导航参数，现有演示的默认值保持不变。

+

八组实验，全部保留

+

“同侧差值”是受刺激一侧的 DNa02 放电频率减去对侧。正数仅表示同侧更活跃，不等于已经确定正确的转向指令。每侧各有一个注释为 DNa02 的细胞。

+
前 50 ms 同侧差值（Hz）与恢复期最后 100 ms 的新增下行神经元放电数
强度 / 顺序第一次气味第二次气味第一次恢复第二次恢复
+
+

查看完整时序

+
+

绿线：左 DNa02紫线：右 DNa02橙线：所选群体的平均放电频率

+
等待完整实验记录
+

着色背景为施加气味的阶段，中间及末尾为无输入恢复期。曲线是平滑后的频率估计；是否还在产生放电，请看实际计数。各条件共享相同纵轴范围。

+
当前条件的 DNa02 时段分析
刺激侧前 50 ms 同侧差值 / Hz后 100 ms 同侧差值 / Hz整段右减左 / 放电数
+
+

这个实验能说明什么？

每组依次运行：50 ms 静息 → 150 ms 一侧气味 → 200 ms 恢复 → 150 ms 另一侧气味 → 250 ms 恢复。只有组间恢复到初始状态，组内不会清空脑活动。

强度为每个连接接触点 0.05、0.10、0.175、0.275 mV，兴奋和抑制权重一起缩放。68 个 DM1 嗅觉入口采用固定 150 Hz 随机事件输入；输入幅度不随连接强度改变。所有原始连接行保留，没有额外背景刺激或动作控制器。

这是使用单个固定随机种子的探索实验。输入只近似一条 Or42b/DM1 通路，未重建完整香蕉混合气味。短暂安静不能证明长期稳定，持续放电也不能直接证明记忆。参数选择还需要独立随机种子、其他刺激和闭环行为验证。

完整原始记录 · 预先公开的实验方案 · 硬件证据与复现 · 气味如何接入脑子

+ +
diff --git a/site/sitemap.xml b/site/sitemap.xml index bd5f242..aef8d22 100644 --- a/site/sitemap.xml +++ b/site/sitemap.xml @@ -6,4 +6,5 @@ https://freeman-1984-coder.github.io/flybrain-sdk/reflex.html https://freeman-1984-coder.github.io/flybrain-sdk/fullbrain.html https://freeman-1984-coder.github.io/flybrain-sdk/foraging.html +https://freeman-1984-coder.github.io/flybrain-sdk/odor-calibration.html diff --git a/tests/test_odor_calibration_protocol.py b/tests/test_odor_calibration_protocol.py new file mode 100644 index 0000000..444918a --- /dev/null +++ b/tests/test_odor_calibration_protocol.py @@ -0,0 +1,97 @@ +"""Timing and analyses must distinguish a transient response from persistent bias.""" + +import importlib.util +import sys +from pathlib import Path + +import pytest + + +def protocol(): + scripts = Path(__file__).parents[1] / "scripts" + spec = importlib.util.spec_from_file_location( + "odor_calibration", scripts / "calibrate_fullbrain_odor.py" + ) + module = importlib.util.module_from_spec(spec) + sys.path.insert(0, str(scripts)) + try: + spec.loader.exec_module(module) + finally: + sys.path.pop(0) + return module + + +def test_counterbalanced_schedule_keeps_same_clock_and_unstimulated_recovery(): + p = protocol() + for order in p.ORDERS: + phases = p.phase_schedule(0.1, order) + assert [(s["start_tick"], s["end_tick"]) for s in phases] == [ + (0, 500), + (500, 2000), + (2000, 4000), + (4000, 5500), + (5500, 8000), + ] + assert [s["side"] for s in phases] == [None, order[0], None, order[1], None] + assert sum(s["duration_ms"] for s in phases) == 800 + + +@pytest.mark.parametrize("dt", [0, -0.1, float("nan"), float("inf"), True, 0.3]) +def test_invalid_timestep_cannot_silently_change_the_protocol(dt): + with pytest.raises(ValueError): + protocol().phase_schedule(dt, ("left", "right")) + + +def test_duplicate_side_cannot_be_mistaken_for_counterbalancing(): + with pytest.raises(ValueError): + protocol().phase_schedule(0.1, ("left", "left")) + + +def test_summary_preserves_early_right_response_despite_later_left_bias(): + run = { + "phases": [ + { + "name": "first", + "side": "right", + "duration_ms": 150, + "first_50ms_spikes": {"DNa02_left": 1, "DNa02_right": 2}, + "spikes": {"DNa02_left": 6, "DNa02_right": 3}, + }, + { + "name": "recovery_first", + "side": None, + "duration_ms": 200, + "tail_window_ms": 100, + "last_100ms_spikes": {"DNa02_left": 0, "DNa02_right": 0}, + # A decaying rate estimate alone must not imply continued spikes. + "mean_rates_hz": {"DNa02_left": 8.0, "DNa02_right": 4.0}, + }, + ] + } + result = protocol().summarize_run(run) + assert result["stimuli"][0] == { + "phase": "first", + "side": "right", + "onset_ipsilateral_minus_contralateral_hz": 20.0, + "later_ipsilateral_minus_contralateral_hz": -40.0, + "total_right_minus_left_spikes": -3, + } + assert result["recovery"][0]["new_tail_spikes"] == {"DNa02_left": 0, "DNa02_right": 0} + + +def test_ipsilateral_metric_changes_reference_with_stimulated_side(): + result = protocol().summarize_run( + { + "phases": [ + { + "name": "second", + "side": "left", + "duration_ms": 150, + "first_50ms_spikes": {"DNa02_left": 2, "DNa02_right": 1}, + "spikes": {"DNa02_left": 3, "DNa02_right": 6}, + } + ] + } + ) + assert result["stimuli"][0]["onset_ipsilateral_minus_contralateral_hz"] == 20 + assert result["stimuli"][0]["later_ipsilateral_minus_contralateral_hz"] == -40 diff --git a/tests/test_voxel_arena.py b/tests/test_voxel_arena.py index dc89ed9..f0ec1d9 100644 --- a/tests/test_voxel_arena.py +++ b/tests/test_voxel_arena.py @@ -44,3 +44,20 @@ def test_world_json_checkpoint_replays_odor_contacts_and_pose(): with pytest.raises(ValueError): arena.apply({"speed": 2, "turn": float("nan")}) assert arena.snapshot() == before + + +def test_mirrored_world_swaps_antennae_and_preserves_mirrored_motion(): + # Reflect about z=-2, the initial forward axis. This rules out a built-in + # left preference in antenna geometry or the body's turn convention. + a = VoxelArena(food=(-1.6, 0.5, -0.6), blocks=()) + b = VoxelArena(food=(-1.6, 0.5, -3.4), blocks=()) + assert a.observe()["odor_right"] > a.observe()["odor_left"] + assert b.observe()["odor_left"] > b.observe()["odor_right"] + for _ in range(100): + a.apply({"speed": 1, "turn": 0.8}) + b.apply({"speed": 1, "turn": -0.8}) + assert a.x == pytest.approx(b.x, abs=1e-12) + assert a.z + b.z == pytest.approx(-4, abs=1e-12) + assert a.yaw == pytest.approx(-b.yaw, abs=1e-12) + assert a.observe()["odor_left"] == pytest.approx(b.observe()["odor_right"], abs=1e-12) + assert a.observe()["odor_right"] == pytest.approx(b.observe()["odor_left"], abs=1e-12)