Internal research programme

Project Athena

Developing NIMCP, a neuro-inspired modular control protocol designed for continual learning, heterogeneous intelligence, and structural governance in high-stakes environments.

01. Continual learning

Biologically inspired plasticity and staged training reduce catastrophic forgetting during asynchronous data ingestion.

02. Structural safety

Safety is embedded as architecture and governance, not added later as behavioural alignment alone.

Abstract neural architecture visualisation for the NIMCP Athena research project
NIMCP / Athena research

Structural blueprint

Heterogeneous architecture

NIMCP trains six distinct neural network types as a single research system, using cognitive diversity rather than a monoculture of one model class.

Architectural abstraction of sparse spiking neural pulses as vertical rods in a neutral space

N_TYPE_01

Spiking

Asynchronous pulse-based processing for low-latency, power-aware inference.

Architectural abstraction of liquid neural dynamics as translucent planes and controlled blue contours

N_TYPE_02

Liquid

Continuous-time dynamics for resilient temporal pattern recognition under noise.

Architectural abstraction of convolutional grids as stacked modular blocks with a blue accent plane

N_TYPE_03

Convolutional

Local feature extraction for structured sensory inputs and spatial regularity.

Architectural abstraction of Fourier frequency bands as interlocking neutral planes with blue accents

N_TYPE_04

Fourier

Frequency-domain modelling for long-range patterns and signal decomposition.

Architectural mechanics model representing Hamiltonian energy conservation with balanced arcs

N_TYPE_05

Hamiltonian

Energy-conserving networks for physical simulation and robotics control loops.

Architectural abstraction of adaptive neural modules reconfiguring along measured rails

N_TYPE_06

Adaptive

Plasticity-driven refinement that supports continual learning without reset cycles.

Training telemetry

Node cluster delta-09 / representative stability trace

A measured research aesthetic: performance, stability, and retention expressed through typographic data rather than decorative dashboards.

Gradient stability matrix

Latency: 14ms
Packet loss: 0.0001%

Loss function
0.0034
Epochs
1,458
Validation
99.82%
Thermal
42°C

Public research site

Explore the public NIMCP project.

Read the public materials, technical framing, and project updates on the dedicated NIMCP website.

Visit NIMCP site