Biology subjects

Gür, B.

Publications and source records attributed to Gür, B..

3 recordsLinked to original sources

bioRxiv2024

Neural pathways and computations that achieve stable contrast processing tuned to natural scenes

Natural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals perform accurate visual behaviors, whereas computer vision devices struggle with changing environments. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control in Drosophila, resulting in stable visual processing. We identify the dendrites of specific third order neurons, Tm1 and Tm9, as the site of luminance gain control. The circuitry further involves neurons with wide-field properties, matching computational predictions that local spatial pooling can drive optimal contrast processing in natural scenes where light conditions change rapidly. Experiments and theory argue that a spatially pooled luminance signal achieves luminance gain control via divisive normalization. This process relies on shunting inhibition using the glutamate-gated chloride channel GluCl. Our work describes computationally, algorithmically, and mechanistically, how visual circuits robustly process visual information in dynamically changing, natural scenes.

neuroscience↗

bioRxiv2021

First-order visual interneurons distribute distinct contrast and luminance information across ON and OFF pathways to achieve stable behavior

The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation, because adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing. The fruit fly Drosophila also shows nearly luminance invariant behavior for both ON and OFF stimuli. To achieve this, first-order interneurons L1, L2 and L3 all encode contrast and luminance differently, and distribute information across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2 and L3 are not distinct inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavioral performance in varying conditions.

neuroscience↗

bioRxiv2021

An optimal population code for global motion estimation in local direction-selective cells

Nervous systems allocate computational resources to match stimulus statistics. However, the physical information that needs to be processed depends on the animals own behavior. For example, visual motion patterns induced by self-motion provide essential information for navigation. How behavioral constraints affect neural processing is not known. Here we show that, at the population level, local direction-selective T4/T5 neurons in Drosophila represent optic flow fields generated by self-motion, reminiscent to a population code in retinal ganglion cells in vertebrates. Whereas in vertebrates four different cell types encode different optic flow fields, the four uniformly tuned T4/T5 subtypes described previously represent a local snapshot. As a population, six T4/T5 subtypes encode different axes of self-motion. This representation might serve to efficiently encode more complex flow fields generated during flight. Thus, a population code for optic flow appears to be a general coding principle of visual systems, but matching the animals individual ethological constraints.

neuroscience↗