Beiran Manuel, Litwin-Kumar Ashok
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
bioRxiv. 2024 May 28:2024.02.22.581667. doi: 10.1101/2024.02.22.581667.
We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.
我们开发了一种连接体约束神经网络理论，其中一个“学生”网络经过训练以重现真实“教师”网络的活动，该“教师”网络代表一个有连接体可用的神经系统。与具有无约束连接性的标准范式不同，这里两个网络具有相同的连接性，但生物物理参数不同，反映了神经元和突触特性的不确定性。我们发现，连接体通常不足以约束执行特定任务的网络的动力学，这说明了仅从连接性推断功能的困难。然而，来自一小部分神经元的记录可以消除这种简并性，使学生网络产生与教师网络一致的动力学。我们的理论还可以确定从哪些神经元进行记录能最有效地预测未测量的网络活动。我们的分析表明，连接体约束模型和无约束模型的解空间在性质上是不同的，并提供了一个框架来确定这些模型何时产生一致的动力学。
