16 research outputs found
A causal inference explanation for enhancement of multisensory integration by co-articulation
Stochastic Resonance Crossovers in Complex Networks
Here we numerically study the emergence of stochastic resonance as a mild phenomenon and how this transforms into an amazing enhancement of the signal-to-noise ratio at several levels of a disturbing ambient noise. The setting is a cooperative, interacting complex system modelled as an Ising-Hopfield network in which the intensity of mutual interactions or âsynapsesâ varies with time in such a way that it accounts for, e.g., a kind of fatigue reported to occur in the cortex. This induces nonequilibrium phase transitions whose rising comes associated to various mechanisms producing two types of resonance. The model thus clarifies the details of the signal transmission and the causes of correlation among noise and signal. We also describe short-time persistent memory states, and conclude on the limited relevance of the network wiring topology. Our results, in qualitative agreement with the observation of excellent transmission of weak signals in the brain when competing with both intrinsic and external noise, are expected to be of wide validity and may have technological application. We also present here a first contact between the model behavior and psychotechnical data.The work was supported by the following: Andalusian Regional Government âJunta de AndalucĂa,â project number FQMâ01505; Spanish Science and Innovation Ministry MICINNâFEDER, project number FIS2009â08451; and Spanish Science and Innovation Ministry MICINN-GREIB, project number GREIB.PT_2011_19