Search Results for author:
Found 7 papers, 0 papers with code
Memory and Information Processing in Recurrent Neural Networks
Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory.
Hierarchical Composition of Memristive Networks for Real-Time Computing
Reservoir computing is an approach that takes advantage of collective system dynamics for real-time computing.
Exploring Transfer Function Nonlinearity in Echo State Networks
ESN is a simple neural network architecture in which a fixed recurrent network is driven with an input signal, and the output is generated by a readout layer from the measurements of the network states.
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Echo state networks (ESN), a type of reservoir computing (RC) architecture, are efficient and accurate artificial neural systems for time series processing and learning.
Towards a Calculus of Echo State Networks
In contrast with previous theoretical frameworks, which only reveal an upper bound on the total memory in the system, we analytically calculate the entire memory curve as a function of the structure of the system and the properties of the input and the target function.
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks.
DNA Reservoir Computing: A Novel Molecular Computing Approach
We show that despite using only three coupled oscillators, a molecular reservoir computer could achieve 90% accuracy on a benchmark temporal problem.
