Technology
The brain doesn’t rely solely on synapses between neurons for communication, memory and intelligence. It also uses ephapses, the electric-field coupling between nearby neurons, to coordinate timing and synchronize neural activity.
This often overlooked synchronization layer may be a missing link in how biological intelligence really works.
Ephapsys is the only AI platform to replicate this missing layer, unlocking a suite of patent-pending components to empower and govern the next generation of trusted AI agents.
Ephaptic Coupling
Ephaptic coupling is a non-synaptic interaction in which neurons influence each other’s activity through local extracellular electric fields, enabling indirect modulation of excitability and synchronization of neural activity.
Applied to AI, it is translated into another matrix that adds lightweight, column-wise interactions between units so networks can synchronize activity patterns across layers without heavy recurrent loops. This controlled cross-talk helps stabilize activations, damp spurious oscillations, and align phases across feature groups, yielding smoother dynamics, lower latency under load, and higher throughput on sequence and streaming tasks.
The Ephaptic Coupling Matrix (ECM) can be tuned, biasing the system toward precision or speed, coordinating ensembles, and synchronizing multi-sensor pipelines to improve convergence, sample efficiency, and resilience in noisy settings.
Ephaptic coupling gives a secondary control plane across layers. Model developers can damp noisy activations, synchronize distant feature groups, and bias the entire network toward precision or throughput without re-training gigantic checkpoints.
Neural Dialing
The ECM introduces a field that adjusts activation flow. This enables coordinated behavior across neuron ensembles and allows critical or latency-sensitive pathways to receive prioritized activation, effectively “tuning” layers without modifying weights.
Stability
Column-level ephaptic coupling provides a natural damping effect that reduces activation spikes, suppresses spurious oscillations, and keeps token-level streaming stable even under adversarial inputs or high-load conditions.
Secure Inference
Secure Inference makes sure only trusted users, agents, and devices can run your models. Each call is policy-checked, device posture is attested, and keys are sealed inside tamper-resistant hardware.
Ephaptic control gives a native security plane. Enforcement is wired into the model’s dynamics rather than bolt-on proxies, so posture changes trigger zeroization instantly.
Every session emits a signed trail such as device identity, policy hash and inference metadata, making audits and revocations real-time across cloud and edge.
Pipeline
- Device attests integrity
- DID / certificate resolved
- Keys released to enclave
- Inference signed + logged
Kill Switch
If posture or policy drifts, the session halts, secrets are zeroized, and downstream agents receive revocation events in milliseconds.
Decentralized PKI
Agentic AI needs durable, verifiable identity. The decentralized PKI (dPKI) anchors agents to DIDs on a zero-knowledge ledger, so rotations, credentials, and revocations stay auditable without leaking private metadata.
Identity Backbone
DIDs publish key material and metadata; updates are notarized with zk-proofs so verifiers can trust the state without seeing the payload.
Proof Engine
Succinct proofs compress many credential updates into a single verification, keeping latency low across global fleets.
Governance
Transferable tokens lets credentials securely move between organizations, consumers or devices without losing provenance, trust and safety.
dPKI meets AI
The credential fabric runs across a distributed network: no single CA to compromise, no single region outage that severs trust.
- Resilience – nodes can fail without breaking verification.
- Transparency – certificates and revocations remain auditable.
- Portability – identities traverse clouds, devices, ecosystems.