The 15-Step Cognitive Cycle
MH-FLOCKE processes sensory input and produces motor output through a biologically grounded 15-step cognitive cycle, executed every simulation timestep (200 Hz). This replaces the typical observe-act loop of reinforcement learning with a layered system inspired by vertebrate neuroscience.
- SENSE — Raw sensor values (position, velocity, orientation, joint angles)
- BODY SCHEMA — Efference copy check: did the body move as predicted?
- WORLD MODEL — Spiking predictive model computes prediction error
- EMOTIONS — Valence-arousal from body signals (falls → fear, progress → satisfaction)
- MEMORY — Retrieve similar past episodes via sensomotor pattern matching
- DRIVES — Compute dominant drive (survival, exploration, comfort, social)
- GWT — Global Workspace competition: sensory vs motor vs predictive vs error
- METACOGNITION — Self-monitoring: confidence, consciousness level
- CONSISTENCY — Are predictions, emotions, and memory aligned?
- REWARD — Combined: curiosity + empowerment + drive modulation + emotion
- LEARNING — R-STDP weight updates modulated by reward and prediction error
- SYNAPTOGENESIS — SNN patterns consolidated into concept graph
- HEBBIAN — Co-activation strengthening
- DREAM — Periodic offline replay for memory consolidation
- NEUROMOD — Adjust DA/5-HT/NE/ACh from emotion + metacognition
Neural Architecture
The SNN uses Izhikevich neurons organized in cerebellar populations following the Marr-Albus-Ito model:
| Population | Freenove (560) | Go2 (1,376) | Role |
|---|---|---|---|
| Mossy Fibers (Input) | 48 | 304 | Sensory encoding |
| Granule Cells | 269 | 567 | Sparse expansion |
| Golgi Cells | 47 | 99 | Inhibitory feedback |
| Purkinje Cells | 24 | 24 | Learning substrate (LTD) |
| DCN | 24 | 24 | Motor correction output |
| Motor Hidden | 136 | 286 | R-STDP learning |
| Output | 12 | 72 | Actuator commands |
The cerebellum additionally uses a 4,000-cell granule expansion layer (Marr-Albus-Ito forward model) for motor prediction and correction via climbing fiber error signals.
Motor Control Stack
The motor output is a blend of three components:
- CPG (Central Pattern Generator) — Phase-coupled oscillators provide innate walking rhythm from step 1. No learning needed to walk.
- SNN Actor — Learned corrections from R-STDP. A competence gate smoothly transitions from 90% CPG to 40% CPG / 60% actor as the SNN demonstrates stable locomotion.
- Reflexes — Spinal stretch reflexes, terrain adaptation, vestibular righting reflex. Always active, never learned.
Steering (v0.5.1)
Navigation uses closed-loop PID steering with IMU feedback:
- Camera detects light source → target heading
- IMU provides actual heading (MPU6050 yaw)
- PID controller computes heading error
- Asymmetric stride: left legs longer stride → dog curves right (tank steering)
- I-term eliminates steady-state drift from mechanical asymmetry
This replaces the earlier Z-offset steering which was proven ineffective through hardware isolation tests.
Meta-Learning Loop (v0.5.1)
Four autonomous modules form a closed self-improvement loop:
- Phase A — EpisodeAnalyzer: Compares successful vs unsuccessful navigation events, identifies correlations
- Phase B — StrategyAdapter: Converts insights into parameter adjustments (RT timing, PID gains)
- Phase C — CuriosityExplorer: World model prediction error drives exploration vs exploitation balance
- Phase D — HypothesisGenerator: Generates testable motor hypotheses from insights
Neuromodulation
Four neuromodulatory systems modulate SNN dynamics globally:
- Dopamine (DA) — Reward prediction, R-STDP learning rate
- Serotonin (5-HT) — Emotional valence, patience
- Norepinephrine (NE) — Arousal, exploration vs exploitation
- Acetylcholine (ACh) — Attention, learning rate boost
References
- Izhikevich, E.M. (2003). Simple model of spiking neurons. IEEE Trans. Neural Networks
- Marr, D. (1969). A theory of cerebellar cortex. Journal of Physiology
- Friston, K. (2010). The free-energy principle. Nature Reviews Neuroscience
- Grillner, S. (2003). The motor infrastructure. Nature Reviews Neuroscience
- Rybak, I.A. et al. (2006). Modelling spinal circuitry involved in locomotor pattern generation
- Frémaux, N. & Gerstner, W. (2016). Neuromodulated STDP. Frontiers in Comp. Neuroscience