Starting with small and simple concepts, and gradually introducing complex
and difficult concepts is the natural process of human learning. Spiking Neural
Networks (SNNs) aim to mimic the way humans process information, but current
SNNs models treat all samples equally, which does not align with the principles
of human learning and overlooks the biological plausibility of SNNs. To address
this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into
SNNs, making SNNs learn more like humans and providing higher biological
interpretability. CL is a training strategy that advocates presenting easier
data to models before gradually introducing more challenging data, mimicking
the human learning process. We use a confidence-aware loss to measure and
process the samples with different difficulty levels. By learning the
confidence of different samples, the model reduces the contribution of
difficult samples to parameter optimization automatically. We conducted
experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and
neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are
promising. To our best knowledge, this is the first proposal to enhance the
biologically plausibility of SNNs by introducing CL.

引入课程学习（Curriculum Learning）的 CL-SNN 模型旨在提高脉冲神经网络（Spiking Neural Networks）的生物学可行性，通过使用置信度感知损失来区分和处理具有不同难度级别的样本，模型自动减少难样本对参数优化的贡献，实验证明其有效性。

脉冲神经网络结合课程学习策略的训练

Training of Spiking Neural Network joint Curriculum Learning Strategy

Image synthesis is increasingly being adopted in medical image processing,
for example for data augmentation or inter-modality image translation. In these
critical applications, the generated images must fulfill a high standard of
biological correctness. A particular requirement for these images is global
consistency, i.e an image being overall coherent and structured so that all
parts of the image fit together in a realistic and meaningful way. Yet,
established image quality metrics do not explicitly quantify this property of
synthetic images. In this work, we introduce two metrics that can measure the
global consistency of synthetic images on a per-image basis. To measure the
global consistency, we presume that a realistic image exhibits consistent
properties, e.g., a person's body fat in a whole-body MRI, throughout the
depicted object or scene. Hence, we quantify global consistency by predicting
and comparing explicit attributes of images on patches using supervised trained
neural networks. Next, we adapt this strategy to an unlabeled setting by
measuring the similarity of implicit image features predicted by a
self-supervised trained network. Our results demonstrate that predicting
explicit attributes of synthetic images on patches can distinguish globally
consistent from inconsistent images. Implicit representations of images are
less sensitive to assess global consistency but are still serviceable when
labeled data is unavailable. Compared to established metrics, such as the FID,
our method can explicitly measure global consistency on a per-image basis,
enabling a dedicated analysis of the biological plausibility of single
synthetic images.

在这项研究中，我们引入了两个度量标准，可以在图像的基础上测量合成图像的全局一致性，并且与传统的指标相比，我们的方法可以在每个图像上明确地测量全局一致性，从而实现对单个合成图像的生物合理性的专门分析。

衡量合成医疗图像全局一致性的指标

Metrics to Quantify Global Consistency in Synthetic Medical Images

Dense Associative Memories or modern Hopfield networks permit storage and
reliable retrieval of an exponentially large (in the dimension of feature
space) number of memories. At the same time, their naive implementation is
non-biological, since it seemingly requires the existence of many-body synaptic
junctions between the neurons. We show that these models are effective
descriptions of a more microscopic (written in terms of biological degrees of
freedom) theory that has additional (hidden) neurons and only requires two-body
interactions between them. For this reason our proposed microscopic theory is a
valid model of large associative memory with a degree of biological
plausibility. The dynamics of our network and its reduced dimensional
equivalent both minimize energy (Lyapunov) functions. When certain dynamical
variables (hidden neurons) are integrated out from our microscopic theory, one
can recover many of the models that were previously discussed in the
literature, e.g. the model presented in "Hopfield Networks is All You Need"
paper. We also provide an alternative derivation of the energy function and the
update rule proposed in the aforementioned paper and clarify the relationships
between various models of this class.

提出了一种微观理论，其中只需两体相互作用，就可以有效地描述大型关联内存，具有一定的生物可信度，并实现了能量最小化的动力学。