  The architecture design and multi-scale learning principles of the human
brain that evolved over hundreds of millions of years are crucial to realizing
human-like intelligence. Spiking Neural Network (SNN) based Liquid State
Machine (LSM) serves as a suitable architecture to study brain-inspired
intelligence because of its brain-inspired structure and the potential for
integrating multiple biological principles. Existing researches on LSM focus on
different certain perspectives, including high-dimensional encoding or
optimization of the liquid layer, network architecture search, and application
to hardware devices. There is still a lack of in-depth inspiration from the
learning and structural evolution mechanism of the brain. Considering these
limitations, this paper presents a novel LSM learning model that integrates
adaptive structural evolution and multi-scale biological learning rules. For
structural evolution, an adaptive evolvable LSM model is developed to optimize
the neural architecture design of liquid layer with separation property. For
brain-inspired learning of LSM, we propose a dopamine-modulated
Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term
dopamine regulation and local trace-based BCM synaptic plasticity. Comparative
experimental results on different decision-making tasks show that introducing
structural evolution of the liquid layer, and the DA-BCM regulation of the
liquid layer and the readout layer could improve the decision-making ability of
LSM and flexibly adapt to rule reversal. This work is committed to exploring
how evolution can help to design more appropriate network architectures and how
multi-scale neuroplasticity principles coordinated to enable the optimization
and learning of LSMs for relatively complex decision-making tasks.
