Computation at the edge of chaos: phase transitions and emergent computation
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
On the computational power of circuits of spiking neurons
Journal of Computer and System Sciences
Nonlinear transient computation
Neurocomputing
Analysis and design of echo state networks
Neural Computation
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
Models Wagging the Dog: Are Circuits Constructed with Disparate Parameters?
Neural Computation
Analysis of decision-making behavior based on complex adaptive system
WSEAS TRANSACTIONS on SYSTEMS
Memory in linear recurrent neural networks in continuous time
Neural Networks
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Intrinsic adaptation in autonomous recurrent neural networks
Neural Computation
Simple deterministically constructed cycle reservoirs with regular jumps
Neural Computation
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Re-visiting the echo state property
Neural Networks
Dynamical coherence patterns in neural field model at criticality
Artificial Life and Robotics
Regulation toward self-organized criticality in a recurrent spiking neural reservoir
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Randomly connected networks have short temporal memory
Neural Computation
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A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson statistics or Poisson statistics with a refractory period. The Bayesian and ...