Reservoir computing experiment – a Liquid State Machine with simulated biological constraints (hormones, pain, plasticity)

Built a reservoir computing system (Liquid State Machine) as a learning experiment. Instead of a standard static reservoir, I added biological simulation layers on top to see how constraints affect behavior.

What it actually does (no BS):

– LSM with 2000+ reservoir neurons, Numba JIT-accelerated

– Hebbian + STDP plasticity (the reservoir rewires during runtime)

– Neurogenesis/atrophy reservoir can grow or shrink neurons dynamically

– A hormone system (3 floats: dopamine, cortisol, oxytocin) that modulates learning rate, reflex sensitivity, and noise injection

– Pain : gaussian noise injected into reservoir state, degrades performance

– Differential retina (screen capture → |frame(t) – frame(t-1)|) as input

– Ridge regression readout layer, trained online

What it does NOT do:

– It’s NOT a general intelligence but you should integrate LLM in future (LSM as main brain and LLM as second brain)

– The “personality” and “emotions” are parameter modulation, not emergent

Why I built it:

wanted to explore whether adding biological constraints (fatigue, pain,hormone cycles) to a reservoir computer creates interesting dynamics vs a vanilla LSM. It does the system genuinely behaves differently based on its “state.” Whether that’s useful is debatable.

14 Python modules, ~8000 lines, runs fully local (no APIs).

GitHub: https://github.com/JeevanJoshi2061/Project-Genesis-LSM.git

Curious if anyone has done similar work with constrained reservoir computing or bio-inspired dynamics.