Circuits of the mind
A graph-theoretic result for a model of neural computation
Discrete Applied Mathematics
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Memorization and Association on a Realistic Neural Model
Neural Computation
The Time Course of Visual Processing: From Early Perception to Decision-Making
Journal of Cognitive Neuroscience
A Quantitative Theory of Neural Computation
Biological Cybernetics
Neural Computations That Support Long Mixed Sequences of Knowledge Acquisition Tasks
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
The hippocampus as a stable memory allocator for cortex
Neural Computation
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Over a lifetime, cortex performs a vast number of different cognitive actions, mostly dependent on experience. Previously it has not been known how such capabilities can be reconciled, even in principle, with the known resource constraints on cortex, such as low connectivity and low average synaptic strength. Here we describe neural circuits and associated algorithms that respect the brain's most basic resource constraints and support the execution of high numbers of cognitive actions when presented with natural inputs. Our circuits simultaneously support a suite of four basic kinds of task, each requiring some circuit modification: hierarchical memory formation, pairwise association, supervised memorization, and inductive learning of threshold functions. The capacity of our circuits is established by experiments in which sequences of several thousand such actions are simulated by computer and the circuits created tested for subsequent efficacy. Our underlying theory is apparently the only biologically plausible systems-level theory of learning and memory in cortex for which such a demonstration has been performed, and we argue that no general theory of information processing in the brain can be considered viable without such a demonstration.