We present BioNIC, a multi-layer feedforward neural network for emotion classification, inspired by detailed synaptic connectivity graphs from the MICrONs dataset. At a structural level, we incorporate architectural constraints derived from a single cortical column of the mouse Primary Visual Cortex(V1): connectivity imposed via adjacency masks, laminar organization, and graded inhibition representing inhibitory neurons. At the functional level, we implement biologically inspired learning: Hebbian synaptic plasticity with homeostatic regulation, Layer Normalization, data augmentation to model exposure to natural variability in sensory input, and synaptic noise to model neural stochasticity. We also include convolutional layers for spatial processing, mimicking retinotopic mapping. The model performance is evaluated on the Facial Emotion Recognition task FER-2013 and compared with a conventional baseline. Additionally, we investigate the impacts of each biological feature through a series of ablation experiments. While connectivity was limited to a single cortical column and biologically relevant connections, BioNIC achieved performance comparable to that of conventional models, with an accuracy of 59.77 $\pm$ 0.27% on FER-2013. Our findings demonstrate that integrating constraints derived from connectomics is a computationally plausible approach to developing biologically inspired artificial intelligence systems. This work also highlights the potential of new generation peta-scale connectomics data in advancing both neuroscience modeling and artificial intelligence.

翻译：本文提出BioNIC——一种受MICrONs数据集精细突触连接图启发的多层前馈神经网络，用于情感分类。在结构层面，我们整合了源自小鼠初级视觉皮层（V1）单个皮层柱的架构约束：通过邻接掩码实现的连接性约束、层状组织结构以及表征抑制性神经元的梯度抑制机制。在功能层面，我们实现了生物启发式学习机制：具备稳态调节功能的赫布突触可塑性、层归一化技术、模拟感官输入自然变异性的数据增强策略，以及模拟神经随机性的突触噪声。同时引入卷积层进行空间处理，以模拟视网膜拓扑映射原理。该模型在面部情感识别任务FER-2013上进行性能评估，并与传统基线模型进行对比。此外，我们通过系列消融实验探究了各生物学特征的影响。尽管连接性仅限于单个皮层柱且采用生物学相关连接方式，BioNIC仍取得了与传统模型相当的性能，在FER-2013数据集上达到59.77 $\pm$ 0.27%的准确率。研究结果表明，整合连接组学衍生的约束条件是一种计算可行的生物启发式人工智能系统开发路径。本工作同时揭示了新一代拍尺度连接组学数据在推动神经科学建模与人工智能发展方面的潜力。