13 research outputs found
Adsorption-desorption noise can be used for improving selectivity
Small chemical sensors are subjected to adsorption-desorption fluctuations
which usually considered as noise contaminating useful signal. Based on
temporal properties of this noise, it is shown that it can be made useful if
proper processed. Namely, the signal, which characterizes the total amount of
adsorbed analyte, should be subjected to a kind of amplitude discrimination (or
level crossing discrimination) with certain threshold. When the amount is equal
or above the threshold, the result of discrimination is standard dc signal,
otherwise it is zero. Analytes are applied at low concentration: the mean
adsorbed amount is below the threshold. The threshold is achieved from time to
time thanking to the fluctuations. The signal after discrimination is averaged
over a time window and used as the output of the whole device. Selectivity of
this device is compared with that of its primary adsorbing sites, based on
explicit description of the threshold-crossing statistics. It is concluded that
the whole sensor may have much better selectivity than do its individual
adsorbing sites.Comment: 10 pages, 3 figures, 2 table
Output Stream of Binding Neuron with Feedback
The binding neuron model is inspired by numerical simulation of
Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire
model. In the binding neuron, the trace of an input is remembered for a fixed
period of time after which it disappears completely. This is in the contrast
with the above two models, where the postsynaptic potentials decay
exponentially and can be forgotten only after triggering. The finiteness of
memory in the binding neuron allows one to construct fast recurrent networks
for computer modeling. Recently, the finiteness is utilized for exact
mathematical description of the output stochastic process if the binding neuron
is driven with the Poissonian input stream. In this paper, the simplest
networking is considered for binding neuron. Namely, it is expected that every
output spike of single neuron is immediately fed into its input. For this
construction, externally fed with Poissonian stream, the output stream is
characterized in terms of interspike interval probability density distribution
if the binding neuron has threshold 2. For higher thresholds, the distribution
is calculated numerically. The distributions are compared with those found for
binding neuron without feedback, and for leaky integrator. Sample distributions
for leaky integrator with feedback are calculated numerically as well. It is
oncluded that even the simplest networking can radically alter spikng
statistics. Information condensation at the level of single neuron is
discussed.Comment: Version #1: 4 pages, 5 figures, manuscript submitted to Biological
Cybernetics. Version #2 (this version): added 3 pages of new text with
additional analytical and numerical calculations, 2 more figures, 11 more
references, added Discussion sectio