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

2026-02-12 · Source: Machine Learning ML & Generative AI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

A researcher developed a Liquid State Machine (LSM) based reservoir computing system, named Project Genesis, incorporating simulated biological constraints to explore their impact on system dynamics. This LSM features over 2000 Numba JIT-accelerated reservoir neurons, dynamic rewiring via Hebbian and STDP plasticity, and neurogenesis/atrophy capabilities allowing the reservoir to grow or shrink. It includes a three-float hormone system (dopamine, cortisol, oxytocin) that modulates learning rate, reflex sensitivity, and noise injection, alongside a "pain" mechanism that injects Gaussian noise to degrade performance. Input is processed through a differential retina, and a ridge regression readout layer is trained online. The system, comprising 14 Python modules and approximately 8000 lines of code, runs entirely locally.

Key takeaway

For AI engineers exploring novel neural architectures, this project demonstrates that integrating bio-inspired constraints like hormones and pain into reservoir computing can yield genuinely different system behaviors. You should consider experimenting with such dynamic modulators to move beyond static reservoir designs, potentially leading to more adaptive or robust learning systems, even if immediate utility requires further validation.

Key insights

Simulating biological constraints in reservoir computing creates distinct system dynamics.

Principles

• Biological constraints alter system behavior.
• Dynamic plasticity enhances reservoir adaptability.

Method

An LSM was built with Numba JIT-acceleration, Hebbian/STDP plasticity, neurogenesis, a three-float hormone system, and pain-induced noise, using differential retina input and online ridge regression.

In practice

• Integrate biological constraints into neural models.
• Use Numba for JIT-accelerated neuron simulations.

Topics

• Reservoir Computing
• Liquid State Machines
• Bio-inspired AI
• Neural Plasticity
• Hebbian Learning

Code references

• JeevanJoshi2061/Project-Genesis-LSM

Best for: AI Engineer, Machine Learning Engineer, AI Researcher

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