A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Real-time computation at the edge of chaos in recurrent neural networks
Neural Computation
Movement Generation with Circuits of Spiking Neurons
Neural Computation
Towards cortex sized artificial neural systems
Neural Networks
Movement prediction from real-world images using a liquid state machine
Applied Intelligence
Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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Recurrent connectivity, balanced between excitation and inhibition, is a general principle of cortical connectivity. We propose that balanced recurrence can be achieved by tuning networks near their critical branching (CB) points when spike propagation is formalized as a branching process. We consider critical branching networks as foundations for artificial general intelligence when they are analyzed as reservoir computing models. Our reservoir models are based on principles of metastability and criticality that were developed in statistical mechanics in order to account for long-range correlations in activities exhibited by many types of complex systems. We discuss reservoir models and their computational properties, and we demonstrate their versatility by reviewing a number of applications.