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Towards cortex sized artificial neural systems.

Christopher Johansson·Anders Lansner
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Abstract

We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns. Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.

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AlgorithmsAnimalsArtificial IntelligenceBayes TheoremHumansLearningMental RecallModels, NeurologicalNeocortexNerve NetNeural InhibitionNeuronsJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

Christopher Johansson, Anders Lansner. 2006-07-24. Towards cortex sized artificial neural systems.. https://doi.org/10.1016/j.neunet.2006.05.029

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