Towards cortex sized artificial neural systems.
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.
Johansson
Christopher
C
Department of Numerical Analysis and Computer Science, Royal Institute of Technology, Stockholm, Sweden. cjo@nada.kth.se
Lansner
Anders
A
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D006801:Humans; D007858:Learning / Q000502:physiology; D011939:Mental Recall / Q000502:physiology; D008959:Models, Neurological; D019579:Neocortex / Q000166:cytology* / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology; D009433:Neural Inhibition; D009474:Neurons / Q000502:physiology*