Does the relationship between learning rules and brain alignment generalize across species? We extend our prior finding that untrained CNNs match backpropagation at human V1 by testing the same five learning rules against macaque electrophysiology. The rules are backpropagation (BP), feedback alignment (FA), predictive coding (PC), spike-timing-dependent plasticity (STDP), and an untrained random-weights baseline. The macaque data come from two datasets: MajajHong2015 (V4/IT, 3,200 stimulus presentations, 88/168 neurons) and FreemanZiemba2013 (V1/V2, 135 stimuli, 102/103 neurons). Using RSA with identical model weights from our human study, we find: (1) all models achieve higher alignment with macaque early visual cortex (rho = 0.15-0.30 at V1/V2) than with human fMRI (rho = 0.01-0.08), consistent with the higher signal-to-noise ratio of electrophysiology; (2) STDP and PC produce the highest macaque V1/V2 alignment (rho ~ 0.30 and 0.28), consistent with their leading position among trained rules in human V1; (3) at IT, learning rule rankings show no detectable correlation across species (Kendall's tau = 0.00, p = 1.00), though this null result is expected given that n = 5 provides power only at tau = +/-1.0, and is further confounded by stimulus set differences; (4) a pretrained ResNet-50 (ImageNet) achieves rho = 0.25 at macaque IT, substantially above all custom CNN conditions (rho = 0.07-0.14), suggesting IT alignment is limited by model capacity and training data rather than by the learning rule. Noise ceilings, multi-seed variability (5 seeds), and a stimulus-control analysis are reported. These results demonstrate that early visual alignment is robust across species, while higher-area alignment is modulated by model capacity and stimulus domain.

翻译：学习规则与大脑对齐的关系能否跨物种泛化？我们通过测试相同五种学习规则与猕猴电生理数据的对齐，将先前未经训练CNN在人类V1区匹配反向传播的发现进行扩展。这些规则包括：反向传播（BP）、反馈对齐（FA）、预测编码（PC）、脉冲时序依赖可塑性（STDP）及随机权重基线。猕猴数据来自两个数据集：MajajHong2015（V4/IT区，3200次刺激呈现，88/168个神经元）和FreemanZiemba2013（V1/V2区，135个刺激，102/103个神经元）。使用与人类研究相同模型权重的RSA分析发现：（1）所有模型在猕猴早期视觉皮层（V1/V2区rho=0.15-0.30）的对齐度均高于人类fMRI（rho=0.01-0.08），这与电生理数据信噪比更高一致；（2）STDP和PC在猕猴V1/V2区对齐度最高（rho≈0.30和0.28），与它们在人类V1区训练规则中的领先地位一致；（3）在IT区，学习规则排序显示跨物种无显著相关性（Kendall tau=0.00，p=1.00），但鉴于n=5仅在tau=±1.0时具有统计效力，且受刺激集差异的混淆，该零结果符合预期；（4）预训练ResNet-50（ImageNet）在猕猴IT区获得rho=0.25，显著高于所有定制CNN条件（rho=0.07-0.14），表明IT区对齐受限于模型容量和训练数据而非学习规则。本文报告了噪声上限、多种子变异性（5个种子）及刺激控制分析。这些结果表明：早期视觉对齐具有跨物种稳健性，而高级皮层区域对齐则受模型容量和刺激域调控。