The :20Brain-state-in-a-box" Neural model is a gradient descent algorithm
Journal of Mathematical Psychology
Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Stability and optimization analyses of the generalized brain-state-in-a-box neural network model
Journal of Mathematical Psychology
Dynamics of complex systems
Situated Cognition: On Human Knowledge and Computer Representations
Situated Cognition: On Human Knowledge and Computer Representations
Space-vector structure based synthesis for hierarchically coupled associative memories
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Mental models: a theoretical overview and preliminary study
Journal of Information Science
Attractor Landscapes and Active Tracking: The Neurodynamics of Embodied Action
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Associative Learning on a Continuum in Evolved Dynamical Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A model for hierarchical associative memories via dynamically coupled GBSB neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach
IEEE Computational Intelligence Magazine
Neural models I: A hierarchical model of neocortical synaptic organization
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
Dynamical analysis of the brain-state-in-a-box (BSB) neural models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Synthesis of Brain-State-in-a-Box (BSB) based associative memories
IEEE Transactions on Neural Networks
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This paper presents information storage and retrieval analysis as well as energy analysis of a multi-level or hierarchically coupled associative memory based on coupled generalised-brain-state-in-a-box (GBSB) neural networks. In this model, the memory processes are described as being organised functionally in hierarchical levels where higher levels coordinate sets of functions of the lower levels. We consider the case where lowest level subnetworks have predefined attractors, prior to imposing their association through imprinting synapses between them. Simulations are carried out using linearly independent (Li) and orthogonal vectors considering a wide range of parameters. The results obtained show that, even when the neural networks are weakly coupled, the system still presents a significant convergence to global patterns, mainly in orthogonal vectors.