The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
The basins of attraction of a new Hopfield learning rule
Neural Networks
Knowledge-based neurocomputing
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Increasing the Capacity of a Hopfield Network without Sacrificing Functionality
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
A Computational Model of Information Processing in the Frontal Cortex and Basal Ganglia
Journal of Cognitive Neuroscience
A model of spatial map formation in the hippocampus of the rat
Neural Computation
An oscillatory hebbian network model of short-term memory
Neural Computation
Networks of the Brain
From an executive network to executive control: A computational model of the n-back task
Journal of Cognitive Neuroscience
Connectionism, Controllers, and a Brain Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A biologically realistic cleanup memory: Autoassociation in spiking neurons
Cognitive Systems Research
Cognitive Systems Research
An attractor network model of serial recall
Cognitive Systems Research
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Many neural network models of cognition rely heavily on the modeler for control over aspects of model behavior, such as when to learn and whether an item is judged to be present in memory. Developing neurocomputational methods that allow these cognitive control mechanisms to be performed autonomously has proven to be surprisingly difficult. Here we present a general purpose framework called GALIS that we believe is amenable to developing a broad range of cognitive control models. Models built using GALIS consist of a network of interacting ''regions'' inspired by the organization of primate cerebral cortex. Each region is an attractor network capable of learning temporal sequences, and the individual regions not only exchange task-specific information with each other, but also gate the others' functions and interactions. As a result, GALIS models can learn both task-specific content and also the necessary cognitive control procedures (instructions) needed to perform a task in the first place. As an initial test of this approach, we use GALIS to implement a model that is trained simultaneously to perform five versions of the n-Back task. Not only does the resulting n-Back model function correctly, determining when to learn or remove items in working memory, but its accuracy and response times correlate strongly with those of human subjects performing the same task. The n-Back model also makes testable predictions about how human accuracy would be affected by intra-trial changes in n's value. We conclude that GALIS opens a potentially effective pathway toward developing a range of cognitive control models with improved autonomy.