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Input weights of the network were used to discern the least significant features (corresponding to the weakest input weights).
Noise imposes a limit on the weakest input that can be detected, and output ultimately saturates as the strength of the input grows.
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The network effectively ignores the weaker input over location (textbf{x}_{2}).
The initial state is set to the desired activity pattern such that the active unit is the unit driven by the weaker input.
This property of the saturation indicator concept is exploited by augmenting the LPV controller by a damper force reconfiguration which explores the weak input redundancy provided by four semi-active dampers.
For the same reason, the Weak Input Signal Data had an initial condition closer to the steady state.
The Weak Input Signal Data had FRAPs that were half of the strength of the FRAPs used to generate the Original Data.
Young DGCs display a high input resistance, low threshold potential for spiking, low threshold currents, and large EPSPs relative to the weak glutamatergic input.
If the fixed point exists, it is always stable and corresponds to the network encoding only the location with the weaker external input ((textbf{x}_{2})).
Together with the morphological evidence of low spine density in immature DGCs (21 dpi: 0.78±0.10 spines/µm, mature: 2.40±0.06 spines/µm, N = 19 neurons for both; V.C. Piatti and A.F.S. unpublished observations; see also [29]) our observations indicate that the weak glutamatergic input is due to a limited number of afferent terminals impinging onto young neurons.
Thanks to the ability of enhancing weak input and suppressing the noise, the stochastic resonance (SR) has been applied to weak signal detection in some fields, and the improvement on its performance are still being concerned, especially in the mechanical engineering.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com