A neurally-inspired agent built in Nengo/NEF that navigates a colored grid world, learns transition statistics, and exhibits curiosity-driven exploration.
- RGB input with Gaussian noise (σ=0.01)
- Opponent color transformation (red-green, blue-yellow, luminance)
- 4D cortical color space
- Cosine similarity classification (threshold 0.4)
- Short-term: Leaky integrator (0.9 decay) for previous color
- Transition detection: Outer product → 25-dimensional space
- Long-term: Integrators (0.99 recurrence) for red-origin transitions
- Reflexive: Wall avoidance from 3 proximity sensors
- Cognitive: Curiosity bias toward less-familiar color transitions
- Familiarity = count_red→X / total_red_transitions
- Bias = -0.6 × familiarity (soft repulsion)
- Arbitration: Priority scaling based on wall distance (emergency > moderate > open)
- Stable color recognition under moderate noise
- Accurate transition counting for red-origin sequences
- Observable exploration diversification in open environments
- Nengo / NEF
- Python
- Neural population coding
- Dynamical systems
- Hopfield networks for context-sensitive memory
- Predictive processing / prediction error signals
- Multi-modal integration
- Long-term adaptation via synaptic plasticity
Chiara Benini