Natural gradient works efficiently in learning
Neural Computation
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Real-time computation at the edge of chaos in recurrent neural networks
Neural Computation
Rocking Stamper and Jumping Snakes from a Dynamical Systems Approach to Artificial Life
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
Complex and Adaptive Dynamical Systems: A Primer
Complex and Adaptive Dynamical Systems: A Primer
A gradient rule for the plasticity of a neuron’s intrinsic excitability
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A self-organized neural comparator
Neural Computation
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A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.