In this survey, we examine algorithms for conducting credit assignment in
artificial neural networks that are inspired or motivated by neurobiology,
unifying these various processes under one possible taxonomy. Our proposed
taxonomy is constructed based on how a learning algorithm answers a central
question underpinning the mechanisms of synaptic plasticity in complex adaptive
neuronal systems: where do the signals that drive the learning in individual
elements of a network come from and how are they produced? In this unified
treatment, we organize the ever-growing set of brain-inspired learning
processes into six general families and consider these in the context of
backpropagation of errors and its known criticisms. The results of this review
are meant to encourage future developments in neuro-mimetic systems and their
constituent learning processes, wherein lies the opportunity to build a strong
bridge between machine learning, computational neuroscience, and cognitive
science.

通过对受神经生物学启发或影响的人工神经网络中进行信用分配的算法的调查，我们提出了一个分类法，根据学习算法回答复杂自适应神经系统突触可塑性机制的核心问题的方式，整理了不断增长的大脑启发学习过程集合为六个通用家族，并在反向传播和其已知批评的背景下考虑了这些家族，结果旨在鼓励未来在神经仿真系统及其组成学习过程的发展，从而在机器学习、计算神经科学和认知科学之间建立良好的桥梁。

脑启发的机器智能：神经生物学可行的信用分配综述

Brain-Inspired Machine Intelligence: A Survey of  Neurobiologically-Plausible Credit Assignment

What does it mean to know language? Since the Chomskian revolution, one
popular answer to this question has been: to possess a generative grammar that
exclusively licenses certain syntactic structures. Decades later, not even an
approximation to such a grammar, for any language, has been formulated; the
idea that grammar is universal and innately specified has proved barren; and
attempts to show how it could be learned from experience invariably come up
short. To move on from this impasse, we must rediscover the extent to which
language is like any other human behavior: dynamic, social, multimodal,
patterned, and purposive, its purpose being to promote desirable actions (or
thoughts) in others and self. Recent psychological, computational,
neurobiological, and evolutionary insights into the shaping and structure of
behavior may then point us toward a new, viable account of language.

探讨语言的本质及其生成语法对特定句法结构的授权方式等方面的问题，指出重新发现语言作为人类行为的本质并了解它的动态、社会、多模式、有意义、意图在于在他人和自己中促进可取的行动（或思维）可能可能指向语言的新的，可行的解决方案。

无句法结构的言语行为：跨越斯金纳和乔姆斯基

Verbal behavior without syntactic structures: beyond Skinner and Chomsky

The fruit fly Drosophila's olfactory circuit has inspired a new locality
sensitive hashing (LSH) algorithm, FlyHash. In contrast with classical LSH
algorithms that produce low dimensional hash codes, FlyHash produces sparse
high-dimensional hash codes and has also been shown to have superior empirical
performance compared to classical LSH algorithms in similarity search. However,
FlyHash uses random projections and cannot learn from data. Building on
inspiration from FlyHash and the ubiquity of sparse expansive representations
in neurobiology, our work proposes a novel hashing algorithm BioHash that
produces sparse high dimensional hash codes in a data-driven manner. We show
that BioHash outperforms previously published benchmarks for various hashing
methods. Since our learning algorithm is based on a local and biologically
plausible synaptic plasticity rule, our work provides evidence for the proposal
that LSH might be a computational reason for the abundance of sparse expansive
motifs in a variety of biological systems. We also propose a convolutional
variant BioConvHash that further improves performance. From the perspective of
computer science, BioHash and BioConvHash are fast, scalable and yield
compressed binary representations that are useful for similarity search.

基于果蝇嗅觉电路启发的两种哈希算法：FlyHash 和 BioHash，分别产生稀疏的高维哈希码，在数据驱动的过程中被证明是高效的相似性搜索方法，提出了结合生物学机制的 LSH 假设，是计算机科学领域的重要研究成果。