2,127 research outputs found
PROCEEDINGS OF THE WORKSHOP ON LARGE-SCALE COMPUTATIONAL PHYSICS ON MASSIVELY-PARALLEL COMPUTERS - HLRZ, KFA-JULICH, GERMANY - JUNE 14 - 16, 1993 - PREFACE
No full textHERRMANN HJ, Karsch F. PROCEEDINGS OF THE WORKSHOP ON LARGE-SCALE COMPUTATIONAL PHYSICS ON MASSIVELY-PARALLEL COMPUTERS - HLRZ, KFA-JULICH, GERMANY - JUNE 14 - 16, 1993 - PREFACE. International Journal of Modern Physics C. 1993;4(6):R3-R4
Activity-dependent neuronal model on complex networks
No full textNeuronalavalanchesareanovelmodeofactivityinneuronalnetworks,experimentallyfoundinvitroandinvivo,andexhibitarobustcriticalbehavior:theseavalanchesarechar-acterizedbyapowerlawdistributionforthesizeandduration,featuresfoundinotherproblemsinthecontextofthephysicsofcomplexsystems.Wepresentarecentmodelinspiredinself-organizedcriticality,whichconsistsofanelectricalnetworkwiththresholdfiring,refractoryperiod,andactivity-dependentsynapticplasticity.Themodelreproducesthecriticalbehaviorofthedistributionofavalanchesizesanddurationsmeasuredexperi-mentally.Moreover,thepowerspectraoftheelectricalsignalreproduceveryrobustlythepowerlawbehaviorfoundinhumanelectroencephalogram(EEG)spectra.Weimplementthismodelonavarietyofcomplexnetworks,i.e.,regular,small-world,andscale-freeandverifytherobustnessofthecriticalbehavior
ABSTRACTS FROM THE CECAM WORKSHOP ON COMPUTER-SIMULATIONS OF CELLULAR AUTOMATA - ORSAY, SEPTEMBER 26 OCTOBER 7, 1988
No full text
Self-organized criticality on small world networks
No full textWe study the BTW-height model of self-organized criticality on a square lattice with some long-range connections giving to the lattice the character of small world network. We find that as function of the fraction p of long-ranged bonds the power law of the avalanche size and lifetime distribution changes following a crossover scaling law with crossover exponents 2/3 and 1 for size and lifetime, respectively. (C) 2002 Elsevier Science B.V. All rights reserved
Optimal percentage of inhibitory synapses in multi-task learning
No full textPerforming more tasks in parallel is a typical feature of complex brains. These are characterized by the
coexistence of excitatory and inhibitory synapses, whose percentage in mammals is measured to have a
typical value of 20-30%. Here we investigate parallel learning of more Boolean rules in neuronal networks.
We find that multi-task learning results from the alternation of learning and forgetting of the individual
rules. Interestingly, a fraction of 30% inhibitory synapses optimizes the overall performance, carving a
complex backbone supporting information transmission with a minimal shortest path length. We show that
30% inhibitory synapses is the percentage maximizing the learning performance since it guarantees, at the
same time, the network excitability necessary to express the response and the variability required to confine the employment of resources
- …
