Department of Physics and Astronomy, York University, Toronto, ON, Canada.
Centre for Vision Research, York University, Toronto, ON, Canada.
Nat Commun. 2024 Jul 16;15(1):5957. doi: 10.1038/s41467-024-50114-5.
Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on 'Deep Retina,' a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.
适应是神经系统的一个普遍特征，它改变了电路计算以匹配当前的输入。这些变化有助于有效地对感觉输入进行编码，同时避免饱和。传统的人工神经网络 (ANNs) 的自适应能力有限，阻碍了它们在动态输入条件下可靠地预测神经输出的能力。将神经自适应机制嵌入到 ANNs 中是否可以提高它们的性能？为了回答这个问题，我们开发了一种新的深度学习模型，该模型将光感受器适应的生物物理学融入到传统卷积神经网络 (CNNs) 的前端。这些传统的 CNN 建立在“深度视网膜”模型的基础上，该模型是视网膜神经节细胞 (RGC) 活动的先前开发的模型。包含这种新的光感受器层的 CNN 在预测雄性和雌性灵长类动物和大鼠 RGC 对自然刺激的反应方面优于传统的 CNN 模型，这些自然刺激包括动态局部强度变化和环境光照的大幅变化。这些改进的预测直接源于光转导级联中的适应。这项研究强调了在 ANNs 中嵌入神经适应模型并使用它们来确定神经电路如何处理动态和广泛的光水平范围内的自然输入编码的复杂性的潜力。
