A two layer model of a complex neuron.

<p>The image (<b><i>x</i></b>) is filtered using a set of <b><i>n</i></b> basis functions (<b><i>w</i></b>_{<b>1…<i>n</i></b>}) and combined into a single response by the non-linear function, <b><i>f</i></b>.</p

Example components learned using ISA.

<p>Each component is shown as a rectangular pair, the left half of each pair shows the receptive field (25 by 25 pixels square) in the left view, the right half of the rectangle shows the right view’s receptive field. Light areas of receptive field are excitatory and black areas inhibitory, grey areas are neural.</p

Properties of different models of binocular integration.

<p>The top row shows examples of bar and sine-grating stimuli (B). Subplot C, an ideal disparity detector responds strongly to a particular disparity and weakly elsewhere. Lines of equal disparity lie on the diagonal with zero disparity (shown as the thick-line) on the main diagonal. D&E, responses of the standard binocular energy model (21) to bar (D) and sine-grating (E) type stimuli. As before, zero disparities lie on the main diagonal. The strongest responses lie on the diagonal where disparities are zero, however strong responses also appear on sidebands at disparities of ±π. F&G, responses of simple-cell models to bar (F) and sine-grating (G) stimuli. Energy is concentrated in locations corresponding to specific combinations of positions in the receptive fields. Disparity in simple-cells is confounded with local spatial position.</p

Bootstrapped histogram of the phase of the disparity response functions for each complex model with a DDI greater than 0.6.

<p>Phases of 0 and π radians show even-symmetric disparity functions and indicate TE (0) and TI (π). All complex cell model with a DDI greater than 0.6 show TI and TE cell exclusively. Cells at lower DDI are not disparity tuned.</p

Distribution of DDI scores for max-pooled complex cell models.

<p>The distribution of DDI scores is bootstrapped 200 times to produce 95% Confidence Intervals (vertical black lines) and median scores (blue boxes) for each cell in a histogram. The max-pooling model exhibits a wide range of binocular disparities between 0 and 0.8743.</p

Example responses to binocular sine-gratings for 25 individual complex models learned using ISA.

<p>The models consist of two linear subunits combined in a sum of squares manner. The phase of the left view sine-grating is shown on the horizontal axis, the right view phase is shown on the horizontal axis. The phases in both views vary between −<b><i>π</i></b> and <b><i>π</i></b>. 100 samples were taken within this range for each view resulting in a 100x100 response map. Locations of equal disparity lie on diagonal lines, locations of zero-disparity are shown as a black line.</p

Responses of complex cell models using the max-pooling model to both sine-gratings and bar stimuli.

<p>Columns 1,3,5 show the responses of the models to sine-gratings stimuli, columns 2,4,6 show responses of the models in the odd columns to bar-stimuli. The models are chosen for their high DDI. As with the energy model (see Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150117#pone.0150117.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150117#pone.0150117.g006" target="_blank">6</a>) the strongest responses lie on diagonals of equal disparity, however unlike the energy model the response function are less smooth, with greater range of responses exhibited on the diagonals of both the sine-grating responses and the bar stimuli responses.</p

Extended examples of complex ISA models’ responses to sine-gratings.

<p>Four complex model responses are shown as combinations of two linear subunits. The two left-hand sine-grating plots show the simple linear responses of individual subunits to sine-gratings varying between–π and π in each view. The right gratings (after the >) show the complex model response (sum of squares of the subunits). The complex models are chosen to illustrate different ‘types’ of complex cell model, A & B show models with high binocular disparity discrimination scores (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150117#sec020" target="_blank">DDI</a> section). In both models the sub-units exhibit strong binocular tuning in phase, resulting in a strong response to particular left/right phase combinations and low responses elsewhere. Phase separation of approximately π⁄2 between the two sub-units results in a quadrature pairing and consistently strong response to a particular disparity. C & D show models with low DDI. In model C, both sub-units are monocular (responses modulated only one eye) resulting in a monocular complex cell. Model C is not phase invariant however the DDI index is not sensitive to monocular phase invariance, other monocular phase invariant models may exist. Model D also shows monocular sub-units, however in contrast to model C the sub-unit are differently monocular in each eye. Model D does not show any invariance in phase and appears to be specialised to detect particular phase combinations.</p

Comparison of cumulative distributions of DDI for both max-pooled and energy model complex cell models.

<p>The cumulative distribution for the max-pooled model is shown in green and the cumulative distribution of the energy model DDI is shown in red. Confidence intervals are generated using 200 bootstrapped distributions, the median of the distributions is shown as a black line in both models, the 95% CI shown as a green (max-pooled) or red (energy model) band. In the lower half for the distribution (DDI values below 0.5) no significant deviation is found between the two distributions. In the upper half a marked bias emerges, with the energy model significantly higher than the max-pooled model.</p

The Evaluation of Externalities

This bachelor thesis focuses on problem of externalities of manufacturing company which should influence a surrounding of company in positive or negative way. The main issue of this thesis is an emergence of externalities in process of production and consumption. This thesis describes a types of those externalities and a ways of solutions. The externalities and their evaluation are presented on specific company, thermal power station in Hodonín. This thesis also contains a results of questionnaire survey among residents of city Hodonín