Recent advances in machine learning have made revolutionary breakthroughs in
computer games, image and natural language understanding, and scientific
discovery. Foundation models and large-scale language models (LLMs) have
recently achieved human-like intelligence thanks to BigData. With the help of
self-supervised learning (SSL) and transfer learning, these models may
potentially reshape the landscapes of neuroscience research and make a
significant impact on the future. Here we present a mini-review on recent
advances in foundation models and generative AI models as well as their
applications in neuroscience, including natural language and speech, semantic
memory, brain-machine interfaces (BMIs), and data augmentation. We argue that
this paradigm-shift framework will open new avenues for many neuroscience
research directions and discuss the accompanying challenges and opportunities.

机器学习、基础模型、大规模语言模型、神经科学研究、生成性人工智能模型的最新进展以及这些模型在神经科学中的应用。

大规模基础模型与生成型人工智能用于大数据神经科学

Large-scale Foundation Models and Generative AI for BigData Neuroscience

Large Language Models (LLMs) do not differentially represent numbers, which
are pervasive in text. In contrast, neuroscience research has identified
distinct neural representations for numbers and words. In this work, we
investigate how well popular LLMs capture the magnitudes of numbers (e.g., that
$4 < 5$) from a behavioral lens. Prior research on the representational
capabilities of LLMs evaluates whether they show human-level performance, for
instance, high overall accuracy on standard benchmarks. Here, we ask a
different question, one inspired by cognitive science: How closely do the
number representations of LLMscorrespond to those of human language users, who
typically demonstrate the distance, size, and ratio effects? We depend on a
linking hypothesis to map the similarities among the model embeddings of number
words and digits to human response times. The results reveal surprisingly
human-like representations across language models of different architectures,
despite the absence of the neural circuitry that directly supports these
representations in the human brain. This research shows the utility of
understanding LLMs using behavioral benchmarks and points the way to future
work on the number of representations of LLMs and their cognitive plausibility.

本文研究大型语言模型（LLMs）是否能够准确地表示数字，并发现尽管缺少直接支持这些表示的神经电路，不同结构的 LLMs 中存在着类似于人类语言使用者的数字表示，这证明了使用行为基准理解 LLMs 的实用性，并为今后关于 LLMs 数字表示和它们的认知合理性的研究指出了方向。

大型语言模型中的数字大小比较效应

Numeric Magnitude Comparison Effects in Large Language Models

Brain-computer interfaces (BCIs), is ways for electronic devices to
communicate directly with the brain. For most medical-type brain-computer
interface tasks, the activity of multiple units of neurons or local field
potentials is sufficient for decoding. But for BCIs used in neuroscience
research, it is important to separate out the activity of individual neurons.
With the development of large-scale silicon technology and the increasing
number of probe channels, artificially interpreting and labeling spikes is
becoming increasingly impractical. In this paper, we propose a novel modeling
framework: Adaptive Contrastive Learning Model that learns representations from
spikes through contrastive learning based on the maximizing mutual information
loss function as a theoretical basis. Based on the fact that data with similar
features share the same labels whether they are multi-classified or
binary-classified. With this theoretical support, we simplify the
multi-classification problem into multiple binary-classification, improving
both the accuracy and the runtime efficiency. Moreover, we also introduce a
series of enhancements for the spikes, while solving the problem that the
classification effect is affected because of the overlapping spikes.

本文提出了一种自适应对比学习模型，通过最大化互信息损失函数进行对比学习，将多分类问题简化为多个二分类问题，同时引入一系列增强措施解决重叠尖峰影响分类效果的问题，提高了运行时效率和准确性。