Encoding of yaw in the presence of distractor motion: studies in a fly motion sensitive neuron

Quantitative Characterization of Output from the Directionally Selective Visual Interneuron H1 in the Grey Flesh Fly Sarcophaga bullata

H1, a very well-studied insect visual interneuron, has a panoramic receptive field and is directionally selective in responding to optic flow. The synaptic basis for the directional selectivity of the H1 neuron has been studied using both theoretical and cellular approaches. Extracellular single-unit recordings are readily obtained by beginning students using commercially available adults of the grey flesh fly Sarcophaga bullata. We describe an apparatus which allows students to present a series of moving visual stimuli to the eye of the restrained, minimally dissected adult Sarcophaga, while recording both the single unit responses of the H1 neuron and the position and velocity of the moving stimulus. Students obtain quantitative and reproducible responses of H1, probing the response properties of the neuron by modulating stimulus parameters such as: direction and speed of movement, visual contrast, spatial wavelength, or the extent of the visual field occupied. Students learn to perform quantitative analysis of their data and to generate graphical representations of their results characterizing the tuning and receptive field of this neuron. This exercise demonstrates the utility of single unit recording of an identified interneuron in an awake restrained insect and promotes interpretation of these results in terms of the visual stimuli normally encountered by freely flying flies in their natural environment.

The impulse response of optic flow-sensitive descending neurons to roll m-sequences

When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow-sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensorimotor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. Therefore, here we used m-sequences to quantify the impulse responses of optic flow-sensitive descending neurons in male Eristalis tenax hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5–18.0 ms. This is similar to the impulse response time to peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.

Summary: The impulse response of hoverfly optic flow-sensitive descending neurons to roll m-sequences reaches its time to peak within 20 ms and slowly returns to baseline over the next 100 ms.

Optimal Local Estimates of Visual Motion in a Natural Environment

Many organisms use visual signals to estimate motion, and these estimates typically are biased. Here, we ask whether these biases may reflect physical rather than biological limitations. Using a camera-gyroscope system, we sample the joint distribution of images and rotational motions in a natural environment, and from this distribution we construct the optimal estimator of velocity based on local image intensities. Over most of the natural dynamic range, this estimator exhibits the biases observed in neural and behavioral responses. Thus, imputed errors in sensory processing may represent an optimal response to the physical signals sampled from the environment.