Sophisticated machine learning struggles to transition onto battery-operated
devices due to the high-power consumption of neural networks. Researchers have
turned to neuromorphic engineering, inspired by biological neural networks, for
more efficient solutions. While previous research focused on artificial neurons
and synapses, an essential component has been overlooked: dendrites. Dendrites
transmit inputs from synapses to the neuron's soma, applying both passive and
active transformations. However, neuromorphic circuits replace these
sophisticated computational channels with metallic interconnects. In this
study, we introduce a versatile circuit that emulates a segment of a dendrite
which exhibits gain, introduces delays, and performs integration. We show how
sound localisation - a biological example of dendritic computation - is not
possible with the existing passive dendrite circuits but can be achieved using
this proposed circuit. We also find that dendrites can form bursting neurons.
This significant discovery suggests the potential to fabricate neural networks
solely comprised of dendrite circuits.

通过引入一种模拟树突分支节段的多功能电路，本研究揭示了树突在生物感知以及神经网络构建中的重要作用，并证明可以纯由树突电路构建神经元网络。

一个多功能电路用于仿真生物活跃树突，应用于声音定位和神经元模拟

A versatile circuit for emulating active biological dendrites applied to  sound localisation and neuron imitation

How humans infer discrete emotions is a fundamental research question in the
field of psychology. While conceptual knowledge about emotions (emotion
knowledge) has been suggested to be essential for emotion inference, evidence
to date is mostly indirect and inconclusive. As the large language models
(LLMs) have been shown to support effective representations of various human
conceptual knowledge, the present study further employed artificial neurons in
LLMs to investigate the mechanism of human emotion inference. With artificial
neurons activated by prompts, the LLM (RoBERTa) demonstrated a similar
conceptual structure of 27 discrete emotions as that of human behaviors.
Furthermore, the LLM-based conceptual structure revealed a human-like reliance
on 14 underlying conceptual attributes of emotions for emotion inference. Most
importantly, by manipulating attribute-specific neurons, we found that the
corresponding LLM's emotion inference performance deteriorated, and the
performance deterioration was correlated to the effectiveness of
representations of the conceptual attributes on the human side. Our findings
provide direct evidence for the emergence of emotion knowledge representation
in large language models and suggest its casual support for discrete emotion
inference.

本研究利用大型语言模型（LLMs）的人工神经元研究人类情感推断机制，发现 LLMs 基于情感知识的概念结构与人类行为中的离散情感的结构相似，且它基于 14 个概念属性的情感推断依赖程度与人类依赖程度相近，这为情感知识在大型语言模型中的出现提供了直接证据，并建议其对离散情感推断至关重要。