System identification of neural systems: If we got it right, would we know?
System identification of neural systems: If we got it right, would we know?
复制标题
神经系统的系统识别：如果我们做对了，我们会知道吗？
DOI：
10.48550/arxiv.2302.06677
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Brian Cheung
中科院分区：
文献类型：
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作者：
Yena Han;T. Poggio;Brian Cheung
人工神经网络被提议作为大脑部分的模型。将该网络与生物神经元的记录进行比较，并认为在再现神经响应方面的良好性能支持该模型的有效性。一个关键问题是，这种系统识别方法能告诉我们多少关于大脑计算的信息。它是否验证了一个模型架构优于另一个模型架构？我们评估了最常用的比较技术，如线性编码模型和中心内核对齐，通过用已知的地面真实模型替换大脑记录来正确识别模型。系统识别性能是相当可变的，它也取决于独立于地面真实架构的因素，如刺激图像。此外，我们展示了使用功能相似性分数识别更高级别的建筑图案的局限性。
Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model's validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.
影响因子：
4.3
作者：
Jonas E;Kording KP
通讯作者：
Kording KP
DOI：
10.51628/001c.27664
发表时间：
2020-07
期刊：
Neurons, Behavior, Data analysis, and Theory
影响因子：
--
作者：
J. Diedrichsen;Eva Berlot;Marieke Mur;Heiko H. Schütt;Mahdiyar Shahbazi;N. Kriegeskorte
通讯作者：
J. Diedrichsen;Eva Berlot;Marieke Mur;Heiko H. Schütt;Mahdiyar Shahbazi;N. Kriegeskorte