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.

本论文提出了一种新型的 LSM 学习模型，该模型集成了自适应结构进化和多尺度生物学习规则，该方法在不同的决策任务上的实验结果表明引入液态层的结构进化和 DA-BCM 的调节可以提高决策能力，并灵活适应规则反转。

适应性结构演化与生物可实现突触可塑性的循环脉冲神经网络

Adaptive structure evolution and biologically plausible synaptic  plasticity for recurrent spiking neural networks

In this paper, we lay out a novel model of neuroplasticity in the form of a
horizontal-vertical integration model of neural processing. The horizontal
plane consists of a network of neurons connected by adaptive transmission
links. This fits with standard computational neuroscience approaches. Each
individual neuron also has a vertical dimension with internal parameters
steering the external membrane-expressed parameters. These determine neural
transmission. The vertical system consists of (a) external parameters at the
membrane layer, divided into compartments (spines, boutons) (b) internal
parameters in the sub-membrane zone and the cytoplasm with its protein
signaling network and (c) core parameters in the nucleus for genetic and
epigenetic information. In such models, each node (=neuron) in the horizontal
network has its own internal memory. Neural transmission and information
storage are systematically separated. This is an important conceptual advance
over synaptic weight models. We discuss the membrane-based (external) filtering
and selection of outside signals for processing. Not every transmission event
leaves a trace. We also illustrate the neuron-internal computing strategies
from intracellular protein signaling to the nucleus as the core system. We want
to show that the individual neuron has an important role in the computation of
signals. Many assumptions derived from the synaptic weight adjustment
hypothesis of memory may not hold in a real brain. We present the neuron as a
self-programming device, rather than passively determined by ongoing input. We
believe a new approach to neural modeling will benefit the third wave of AI.
Ultimately we strive to build a flexible memory system that processes facts and
events automatically.

本文提出了一种水平垂直一体化的神经处理模型，该模型包含了神经适应传输连接的水平面和由外部膜层表达参数、亚膜区和细胞质内部参数、核心参数等组成的垂直系统，旨在表明单个神经元在信号的计算中有着重要角色，并且该神经元是自我编程的装置，不是被输入决定。

一种新型神经模型的初步构思

Sketch of a novel approach to a neural model

Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming catastrophic forgetting. In this paper, we argue that,
given the limited model capacity and the unlimited new information to be
learned, knowledge has to be preserved or erased selectively. Inspired by
neuroplasticity, we propose a novel approach for lifelong learning, coined
Memory Aware Synapses (MAS). It computes the importance of the parameters of a
neural network in an unsupervised and online manner. Given a new sample which
is fed to the network, MAS accumulates an importance measure for each parameter
of the network, based on how sensitive the predicted output function is to a
change in this parameter. When learning a new task, changes to important
parameters can then be penalized, effectively preventing important knowledge
related to previous tasks from being overwritten. Further, we show an
interesting connection between a local version of our method and Hebb's
rule,which is a model for the learning process in the brain. We test our method
on a sequence of object recognition tasks and on the challenging problem of
learning an embedding for predicting $<$subject, predicate, object$>$ triplets.
We show state-of-the-art performance and, for the first time, the ability to
adapt the importance of the parameters based on unlabeled data towards what the
network needs (not) to forget, which may vary depending on test conditions.

本文提出了一个新的生命周期学习的方法，命名为记忆感知突触 (Memory Aware Synapses)，它计算神经网络参数的重要性，并根据重要性进行惩罚以防止重要知识被覆盖，这与大脑学习过程的 Hebb 规则有相关性。在物体识别任务和嵌入学习任务中，该方法表现出最先进的性能。