Download Linear Mouse

I'm writing a Windows application that uses SendInput to move the mouse without the user having to use an actual mouse. One of its desired features is that it'll create the same movement regardless of what the user's Mouse Speed setting is (assuming they don't have "Enhance pointer precision" checked, so my application shouldn't have to account for "mouse acceleration").

I do this by getting Windows' mouse speed setting and dividing my output by it. So, if the user doubles their mouse speed setting, my application halves its output, and they should cancel out (if the mouse speed setting actually represents a multiplier for the cursor speed).

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The problem is this value isn't linearly proportional to the actual mouse speed with a given input. If it were linearly proportional, if a given mouse movement moves the on-screen cursor by distance X, doubling the mouse speed setting and performing the same mouse movement should result in the on-screen cursor moving a distance of 2X. Halving the setting should result in a 0.5X movement on-screen.

Is there any documentation that shows how to map the Windows mouse speed to an actual speed (whatever the units), suitable for cancelling this setting out? Or is there a better way for me to go about accounting for the user's mouse speed setting, or to ignore the user's mouse settings entirely?

It seems Apple has added a feature that changes the sensitivity of the mouse based on the speed the mouse is moving. This makes it impossible to know where your mouse is going to end up every time you move it. This has made it almost unusable in my case. They should switch this to a simply linear movement, which would make much more sense especially considering many games will likely add mouse support in the coming weeks. Gaming is essentially impossible with mouse acceleration enabled, and I can not wrap my mind around why Apple would add this.

I'm struggling to figure out why the below function is only ever moving my mouse from 0, 0 screen coords, to my final destination, even though Cursor.Position is returning the correct screen coords. If anybody could enlighten me i'd be most grateful.

Neurons in sensory systems encode different features of the environment by integrating signals that are detected in receptor cells. In the visual system, the signal detection occurs in the photoreceptors, which are distributed over space on the retina and which belong to different chromatic channels, depending on their spectral sensitivity. Downstream neurons, therefore, integrate visual information over time and space as well as over color channels, and the signal transformations inherent in these integration processes shape the computation and feature extraction associated with a given neuron. Temporal and spatial signal integration have been intensively studied in the retina, where they have been linked to phenomena such as temporal filtering, adaptation, motion detection, and other specific visual functions1,2,3,4,5,6,7,8,9. Studies of spatial integration, in particular, refined the idea of receptive fields10,11 and later resulted in the distinction of linear and nonlinear spatial integration, as originally exemplified by the X and Y cells of the cat retina12,13,14. Mechanistic investigations of spatial integration then elucidated the role of retinal bipolar cells in shaping signal transmission through the retina15,16,17,18 and helped characterize the suppressive receptive field surround11,19,20,21.

So far, chromatic signal processing has been mostly connected with color-opponent cells, which constitute a specific subpopulation of ganglion cells26,27,28,29,30,31,32,33. But other types of ganglion cells also combine signals from different chromatic channels and could do so in different ways, for example, linearly or nonlinearly. Thus, a thorough understanding of retinal signal integration requires including chromatic integration beyond the studies of color-opponent cells and approaching it similarly to temporal and spatial integration.

Retinal neurons are often functionally characterized by how they integrate input signals over time and space2,14,34. Yet, in addition, retinal neurons also integrate signals chromatically. Photoreceptors separate light into pathways that represent different wavelengths (colors), and downstream neurons pool signals from these chromatic channels. The photoreceptors in the mouse retina, for example, have peak sensitivities in the UV (S-cones) and green range (M-cones and rods). Retinal ganglion cells can therefore combine signals from UV and green light, and this can occur in a linear or nonlinear fashion, depending on the characteristics of signal transmission in the corresponding retinal circuits. To study this chromatic integration, we drew analogies to classical studies of spatial integration12,13,14. These studies investigated the responses of retinal ganglion cells under contrast-reversing spatial gratings (or presentation and withdrawal of the grating) and searched for nulling of responses, that is, no evoked activity for either reversal direction. Response nulling indicated a cancellation of the activation from luminance increases and decreases, which is a sign of linear spatial integration, and the corresponding cells were called X cells. So-called Y cells, on the other hand, displayed increased activity for both reversal directions, taken as a sign for a lack of cancellation and thus nonlinear integration.

Figure 1 shows that such different response characteristics also occur for chromatic integration. The two displayed sample cells from the mouse retina were both Off cells and responded with bursts of spikes when either the green or the UV illumination was decreased (negative contrast; Fig. 1a, b). For a particular contrast combination, however, with decreased illumination of one color and increased illumination of the other (Fig. 1c), Cell 1 remained silent regardless of whether green contrast was positive and UV contrast was negative or whether the contrast-reversed version was applied. Thus, akin to the X cells of spatial integration, positive and negative activation in the two chromatic channels can cancel each other, providing evidence of linear chromatic integration by this cell. Cell 2, on the other hand, displayed vigorous spiking for both of these opposing contrast combinations. Analogous to Y cells, this cell was therefore activated by contrast-reversed stimuli without cancellation, indicating nonlinear chromatic integration.

Responses of mouse retinal ganglion cells were recorded with multielectrode arrays, and stimuli were delivered via a projection system with two LEDs suited for activating mouse photoreceptors (Supplementary Fig. 1). To better separate signals coming from the two cone pathways of the mouse retina, originating in the S- and M-opsins of cone photoreceptors, we used the method of silent substitution35 to present opsin-isolating stimuli. Note, though, that the relative spectral sensitivity of the mouse M-opsin is nearly identical to that of the mouse rod opsin36,37,38. Rods will therefore experience similar effective contrast as the M-opsins and, in particular, will not be activated by S-opsin-isolating stimuli. All color contrast values in this work, therefore, imply contrast on the level of opsin activation, with UV contrast standing for S-opsin activation and green contrast for M-opsin and rod activation.

The chromatic-integration curves allow easy identification of the balance point as the crossing point of the two curves. At this point, one chromatic stimulus combination and its contrast-reversed version induce the same response, indicating balanced input from the two chromatic channels. Whether the response level at the crossing point is near zero or deviates from zero is therefore indicative of whether chromatic integration is linear (Fig. 2e) or nonlinear (Fig. 2f), respectively.

To quantify the occurrence of frequency doubling for each recorded cell as a sign of potential nonlinear chromatic integration under grating stimulation, we defined a grating nonlinearity index. Analogous to indices applied to analyzing spatial integration with reversing gratings12,49,50, we used the signal amplitude of the second harmonic relative to the amplitude of the first. This was taken at the contrast combination that had the lowest first-harmonic amplitude, which indicates stimulation near the balance point. Using this measure, we found that chromatically linear and chromatically nonlinear cells as defined under spatially homogeneous stimulation had similar distributions of the grating nonlinearity index (Fig. 5f). In fact, for all distinguished subpopulations of cells, the large majority of grating nonlinearity indices were smaller than unity, indicating the absence of frequency doubling and thus mostly linear chromatic integration under this stimulus.

To investigate the role of rod photoreceptors in nonlinear chromatic integration, we tested the effect of different light levels by comparing responses to the full-field chromatic-integration stimulus at our standard light level in the mesopic/low photopic regime and at a 10-fold increased intensity (mid-to-high photopic). These experiments showed that higher light levels tended to linearize the chromatically nonlinear cells. This effect was similar for all classes of nonlinear cells (Fig. 9a, b), while linear cells generally remained linear under higher light levels. This is consistent with our hypothesis that the rod pathway is critical for nonlinear chromatic integration.

The measured nonlinear chromatic integration should primarily affect responses to stimuli for which two chromatic signals have opposing contrast. Although this condition is easily met in the experimental setup, the occurrence in natural scenes is unclear. To test what the effects may be for the encoding of natural scenes, we built simple models of linear and nonlinear chromatic integration and passed colored images of natural scenes through each model (Fig. 10a). For concreteness, we simulated nonlinear chromatic integration by filtering the green and UV components of the images separately with Off-type Gaussian receptive fields and combining the two resulting signals after half-wave rectification. This phenomenological model captures the elicited activity under stimuli with opposing contrast in green and UV illumination in a generic fashion without the need to specify a particular circuit mechanism. For comparison, we simulated chromatically linear cells with the same structure, but without the rectification of the two chromatic signals. 2351a5e196