Topic: eigenspectrum

We study randomly connected neural networks to understand the effects of network architectures on network dynamics. Specifically, we examine the impact of sparse connectivity combined with Dale’s law on network stability by analysing the eigenspectrum of the networked Jacobian. Previous work has examined the effects of Dale’s law for fully connected random matrices that are balanced (zero mean) [3] or unbalanced [1] and sparse matrices with constant weights for each population [2]. Our work significantly extends these results by integrating both Dale’s law and sparsity into both balanced and u

Breakthrough in-vivo recording techniques and computational tools allow quantitative characterization of the population geometry from large-scale neural recordings. Activity covariance from such recordings exhibits striking statistical properties, namely a characteristic power law in the eigenspectrum: the decay coefficient of the eigenspectrum α≈1 for mouse and monkey V1 population responses. Concurrently, studies in self-supervised deep neural networks (DNNs) also demonstrate a similar property, i.e., α is close to 1 in their final layers of visual information processing. However, these mod