Bio-base... Bio-inspired.. NMR/MRI
Bio-inspired devices
Problems with artificial neural networks originate from
their deterministic nature and inevitable prior learnings, resulting in
inadequate adaptability against unpredictable, abrupt environmental change.
Here we show that a stochastically excitable threshold unit can be utilized by these systems to partially overcome the environmental change.
Using an excitable threshold system,
attractors were created
that represent quasi-equilibrium states into which a system settles
until disrupted by environmental change.
Furthermore,
noise-driven attractor stabilization and switching were embodied
by inhibitory connections.
Noise works as a power source to stabilize and switch attractors, and
endows the system with hysteresis behavior that resembles that of stereopsis and
binocular rivalry in the human visual cortex.
A canonical model of the ring network with inhibitory connections composed of class 1 neurons also shows properties that are similar to the simple threshold system.
See N.Asakawa et al., Phys.Rev.E, 79, 021902 for the details.