Title :
Spiking brain models: Computation, memory and communication constraints for custom hardware implementation
Author :
Lansner, Anders ; Hemani, Ahmed ; Farahini, Nasim
Author_Institution :
Dept. of Comput. Biol., Stockholm Univ., Stockholm, Sweden
Abstract :
We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within reach of tomorrow´s technology.
Keywords :
belief networks; bioelectric phenomena; biology computing; brain models; computational complexity; neural nets; neurophysiology; BCPNN model; Bayesian Confidence Propagation Neural Network; custom hardware implementation; human neural information processing; problem complexity; spiking brain model communication constraints; spiking brain model computation constraints; spiking brain model memory constraints; Brain models; Computational modeling; Mathematical model; Memory management; Neurons; Real-time systems;
Conference_Titel :
Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific
Conference_Location :
Singapore
DOI :
10.1109/ASPDAC.2014.6742950
