Life Sciences & Healthcare
Neuroscience
Uncover dynamical laws governing neural activity from EEG, fMRI, and electrophysiology recordings.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Neuroscience teams still struggle to explain what is happening
Neuroscience produces some of the most complex time-series data in biology: EEG, fMRI BOLD signals, multi-electrode electrophysiology, and calcium imaging. The underlying dynamics involve regime transitions, oscillatory behavior, nonlinear coupling, and causal connectivity across distributed neural populations. These datasets encode the governing equations of brain function, from single-neuron firing patterns to large-scale network coordination. The dimensionality, temporal complexity, and regime-dependent nature of neural data make extracting these mathematical laws exceptionally difficult through conventional methods.
Spectral decomposition, event-related averaging, general linear models, and independent component analysis reduce data to summary statistics that discard dynamical structure. These methods lose information about how neural systems evolve, transition, and interact over time. Correlation- or coherence-based connectivity analysis cannot distinguish causal influence from shared input or common drive. The regime-dependent nature of neural dynamics, where governing equations change across brain states, violates stationarity assumptions embedded in most standard time-series frameworks.
The CDE Approach
How CDE closes the explanation gap in neuroscience
CDE discovers the governing equations of neural dynamics directly from recording data: the mathematical laws describing how neural populations interact, synchronize, and transition between activity states. Rather than reducing neural data to static summary measures, CDE captures the full dynamical structure as typed scientific claims, including oscillatory coupling, regime transitions, and directed causal interactions. This reveals state-dependent connectivity changes, nonlinear coupling dynamics, and the transition rules governing shifts between distinct activity regimes.
Regime classification automatically identifies distinct brain states and their transition dynamics, essential for understanding how neural systems switch between resting, task-engaged, and pathological states. Symbolic mode discovers oscillatory laws governing neural rhythms and coupling relationships between frequency bands. Causal mode maps effective connectivity (directed causal relationships between brain regions) from observational recordings. Automated negative controls help ensure that discovered connectivity reflects genuine causal influence rather than volume conduction or shared input.
Discovery Engine
How CDE applies here
Causal mode produces directed causal graphs of effective connectivity, resolving the fundamental limitation of correlation-based methods that cannot establish causal direction between brain regions. Symbolic mode discovers closed-form oscillatory equations and coupling laws governing neural rhythms, directly comparable against theoretical models. Neuro-Symbolic mode serves high-dimensional recording modalities like calcium imaging and high-density electrophysiology, where the engine's internal methods capture spatial-temporal structure before structure extraction yields interpretable dynamical laws.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Neural oscillation governing equations
- Brain state regime classifications
- Effective connectivity causal graphs
- Seizure dynamics transition models
- Neural population coupling laws
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For neuroscience, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real neuroscience problem
Whether you are exploring neuroscience data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.