IBM-1

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The Implicit Brain Model is a multimodal foundation model of whole-brain state and dynamics. One parameter set is declared once, and task-specific models are materialized out of it on demand. Because the representation is tied to the brain rather than to an instrument, film, MEG, EEG and motion capture all constrain the same parameters — data that could not otherwise be combined at all.

One Brain, Many Perspectives

Specify a specific region, resolution, or bandwidth and only the processes and variables that actually matter materialize. The same brain can be coarse everywhere and fine around a single electrode.

M = materialize(R, r, B, F, A, T, P)

Designed to Resonate like the Brain

A brain's rhythms are not decoration laid over its anatomy — they are what a loop with a delay and a gain does. IBM-1 declares the loops themselves: which structures, in which order, with what conduction delay. A band is then a consequence to be checked, not a spectrum to be fitted.

Thalamo-Cortical Alpha

Cortex and thalamus drive each other. Fitted to sleep, it finds spindles at 13.45 Hz in people it never saw (p = 7.2e-11).

Hippocampal Theta–Gamma

Each slow theta cycle is cut into fast gamma slots, so an episode's order is carried by timing.

Fronto-Parietal Attention

Visual cortex proposes, parietal cortex maps, frontal cortex picks one place to look.

More circuits

Basal ganglia, cerebellum, default mode, arousal, valuation, hearing, vision and movement, each declared as a loop.

Each figure lights the structures the substrate declares for that loop and runs a wave around it. The on-screen rate is a depiction, not a measured frequency.

64-Channel EEG to Visual Thought Decoding

  • 63.5%image → EEG, top-1
  • 60.0%EEG → image, top-1

Whole Body Human Simulation

Every system the atlas carries — skeleton, muscle, vessels, viscera and the skin over them — on one driven skeleton, with the cortex closing the loop out to the muscle and back.

  • 13 systemsbone, muscle, fascia, artery, vein, lymph, gut, lung, heart, gland, kidney, reproductive tract and skin, each bound to the segment it rides on.
  • 0.17 mmit moves, when you shove it.
  • 2.90 sto the floor, with the cortex cut out.

Control: scramble what the cortex learned and it still stands. The cortex owns the muscles; that is not the same as it knowing how to walk.

Anatomy: Z-Anatomy (CC-BY-SA) on DBCLS BodyParts3D.

Predicting TMS Effects on the Brain

  • where it landsthe field is solved through skull and scalp, so the answer is a place on the cortex rather than a spot on the head.
  • what it does nowthe response every electrode records, milliseconds after the pulse.
  • what it does by week fourhow the rhythms of the targeted region reorganise over a course of treatment.

Try It!

materialize.ipynb
[1]
!pip install git+https://github.com/JacobFV/IBM-1
[2]
import ibm
from ibm.materialize import library

ibm.load_all(seal=True, strict=True)
print(ibm.REGISTRY.summary())
13 fields · 113 components · 22 supports · 20 anatomical systems
17 topologies · 30 processes · 115 implementations
[3]
m = library.MODELS["eeg_forward"]
print(m.request.describe())
request 'eeg-forward'  subject=template  frame=subject_t1
  targets     device.contact_potential, electromagnetic.potential
  regions     cortex, conductor
  r(q)        3 rules, default 6 mm
  B(q)        default Band(0.5-100Hz), 3 overrides
  window      n=2048 dt=0.001s (2.048s, nyquist 500 Hz)
[4]
# one kernel carries the parameters for any materialization
from huggingface_hub import hf_hub_download

ckpt = hf_hub_download("jacob-valdez/ibm-1",
                       "implicit/ibm1.implicit.s30k.e128.fused34.pt")

Open in Colab GitHub The Implicit Kernel Task Checkpoints