Topic: spectral radius

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

Reservoir computing is a machine learning framework in which only the output layer is trained to perform a task; the rest – composed of an input layer and recurrent neural network – has fixed connections. The benefit of restricting training to the output layer is that it does not require the computationally expensive and biologically-implausible backpropagation typically used to train deep-learning networks. However, while reservoirs can do tasks well, their performance is unreliable; depending on the random initialisation of fixed weights within the network, their capabilities vary wildly, si