Visualization 1.mp4

Visualization 1 shows the spectrum of the mode hop free tunable laser with continuously altering while the wave crests passing on the F-P interferometer.</p

The rRMSD values using different particle methods for serial and parallel processing, calculated from 50 repetitions.

In the labels of the x-axis, APFg: APF with geometric mean, iBF: individual decoding using BF, iAPF: individual decoding using APF, mBF: BF with marginal likelihood, mAPF: APF with marginal likelihood, mAFPg: APF with marginal likelihood and geometric mean. For example, APFg-FB means using APF with geometric mean, and reporting estimates using fixed-interval smoothing by the forward-filtering backward-smoothing algorithm.</p

Realizations of spike trains.

The left panels show the three response kernels. The top panels show different types of stimuli. Spike trains are shown for each combination of response kernel and stimulus. Each line represents an independent trial. For each combination, 50 example spike trains are simulated.</p

Summary of results.

The signs ≈, denote decoding performance comparison in different settings.</p

Decoding of stochastic stimulus mixtures from a single spike train.

Decoding by BF with filtering, BF-F (upper panel), fixed-lag smoothing, BF-lag (middle panel) and fixed-interval smoothing, BF-FB (lower panel). The three panels show the decoding of the same spike train. See caption of Fig 3 for explanation.</p

State-space model used for the decoding of stochastic stimuli.

State-space model used for the decoding of stochastic stimuli.</p

Examples of parameter learning of <i>γ</i> over time.

The solid line is the mean of 500 particles, and dashed lines show ± the standard deviation. The red lines are the true value.</p

Characteristics of response kernels used in the encoding model.

Characteristics of response kernels used in the encoding model.</p

Summary of methods.

Summary of methods.</p

Decoding from 20 spike trains using BF assuming parallel processing.

In each spike train, neuronal attention switches at continuous times following a Poisson process.</p