70 citations · 165 across the 13 of their papers we have counts for
7 papers · 1 filter
Memory and Information Processing in Recurrent Neural Networks
Alireza Goudarzi, Sarah Marzen, Peter Banda +3
Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously…
Exploring Transfer Function Nonlinearity in Echo State Networks
Alireza Goudarzi, Alireza Shabani, Darko Stefanovic
Supralinear and sublinear pre-synaptic and dendritic integration is considered to be responsible for nonlinear computation power of biological neurons, emphasizing the role of nonl…
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Alireza Goudarzi, Alireza Shabani, Darko Stefanovic
Echo state networks (ESN), a type of reservoir computing (RC) architecture, are efficient and accurate artificial neural systems for time series processing and learning. An ESN con…
Towards a Calculus of Echo State Networks
Alireza Goudarzi, Darko Stefanovic
Reservoir computing is a recent trend in neural networks which uses the dynamical perturbations on the phase space of a system to compute a desired target function. We present how…
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Alireza Goudarzi, Peter Banda, Matthew R. Lakin +2
Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a…
Learning, Generalization, and Functional Entropy in Random Automata Networks
Alireza Goudarzi, Christof Teuscher, Natali Gulbahce +1
It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a s…