Sensory integration: neuronal adaptations for robust visual self-motion estimation

Noise-robust recognition of wide-field motion direction and the underlying neural mechanisms in Drosophila melanogaster

Appropriate and robust behavioral control in a noisy environment is important for the survival of most organisms. Understanding such robust behavioral control has been an attractive subject in neuroscience research. Here, we investigated the processing of wide-field motion with random dot noise at both the behavioral and neuronal level in Drosophila melanogaster. We measured the head yaw optomotor response (OMR) and the activity of motion-sensitive neurons, horizontal system (HS) cells, with in vivo whole-cell patch clamp recordings at various levels of noise intensity. We found that flies had a robust sensation of motion direction under noisy conditions, while membrane potential changes of HS cells were not correlated with behavioral responses. By applying signal classification theory to the distributions of HS cell responses, however, we found that motion direction under noise can be clearly discriminated by HS cells, and that this discrimination performance was quantitatively similar to that of OMR. Furthermore, we successfully reproduced HS cell activity in response to noisy motion stimuli with a local motion detector model including a spatial filter and threshold function. This study provides evidence for the physiological basis of noise-robust behavior in a tiny insect brain.

Antioxidant activity stimulated by ultraviolet radiation in the nervous system of a crustacean

Ultraviolet (UV) radiation can produce biological damage, principally oxidative stress, by increasing the production of reactive oxygen species (ROS). This study evaluated biochemical impairments related to the oxidative stress induced by UVA, UVB and UVA+UVB (solar simulator-SIM) in environmental doses, during five consecutive days of exposure, in the brain and eyestalk of the crab Ucides cordatus. We evaluated these regions by sampling on the 1st, 3rd and 5th days of UV exposure for lipid peroxidation (LPO), antioxidant capacity against the peroxyl radical (ACAP), and the activities of catalase (CAT), glutathione peroxidase (GPX) and glutathione-S-transferase (GST). Immunohistochemical and immunoblotting assays were performed for anti-activated-caspase 3 in the brains. After the first day of exposure, LPO increased in the eyestalks and brains of the UV-exposed animals; ACAP, and CAT, GPX and GST activities also increased in the brains. On the third day, the LPO values in the eyestalk remained high in the UV-exposed groups, while ACAP decreased in the brain and eyestalk and CAT activity remained high in all irradiated groups in both regions. On the fifth day, LPO decreased in the eyestalk and brain of the UV-exposed groups. These results may have been a consequence of the antioxidant defense system (ADS) activity, since CAT activity was high in both regions, ACAP was high in the eyestalks of the SIM group, and GPX activity remained high in the eyestalks of the UVA and UVB groups. Immunohistochemical assays and immunoblotting showed that there was apoptosis in the brains of the UV-exposed crabs. In conclusion, environmental doses of UV can cause oxidative damage to the CNS cells, including apoptosis.

An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation

Insects carry a pair of antennae on their head: multimodal sensory organs that serve a wide range of sensory-guided behaviors. During locomotion, antennae are involved in near-range orientation, for example in detecting, localizing, probing, and negotiating obstacles. Here we present a bionic, active tactile sensing system inspired by insect antennae. It comprises an actuated elastic rod equipped with a terminal acceleration sensor. The measurement principle is based on the analysis of damped harmonic oscillations registered upon contact with an object. The dominant frequency of the oscillation is extracted to determine the distance of the contact point along the probe and basal angular encoders allow tactile localization in a polar coordinate system. Finally, the damping behavior of the registered signal is exploited to determine the most likely material. The tactile sensor is tested in four approaches with increasing neural plausibility: first, we show that peak extraction from the Fourier spectrum is sufficient for tactile localization with position errors below 1%. Also, the damping property of the extracted frequency is used for material classification. Second, we show that the Fourier spectrum can be analysed by an Artificial Neural Network (ANN) which can be trained to decode contact distance and to classify contact materials. Thirdly, we show how efficiency can be improved by band-pass filtering the Fourier spectrum by application of non-negative matrix factorization. This reduces the input dimension by 95% while reducing classification performance by 8% only. Finally, we replace the FFT by an array of spiking neurons with gradually differing resonance properties, such that their spike rate is a function of the input frequency. We show that this network can be applied to detect tactile contact events of a wheeled robot, and how detrimental effects of robot velocity on antennal dynamics can be suppressed by state-dependent modulation of the input signals.

Recording from two neurons: second-order stimulus reconstruction from spike trains and population coding

We study the reconstruction of visual stimuli from spike trains, representing the reconstructed stimulus by a Volterra series up to second order. We illustrate this procedure in a prominent example of spiking neurons, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. Second-order reconstructions require the manipulation of potentially very large matrices, which obstructs the use of this approach when there are many neurons. We avoid the computation and inversion of these matrices using a convenient set of basis functions to expand our variables in. This requires approximating the spike train four-point functions by combinations of two-point functions similar to relations, which would be true for gaussian stochastic processes. In our test case, this approximation does not reduce the quality of the reconstruction. The overall contribution to stimulus reconstruction of the second-order kernels, measured by the mean squared error, is only about 5% of the first-order contribution. Yet at specific stimulus-dependent instants, the addition of second-order kernels represents up to 100% improvement, but only for rotational stimuli. We present a perturbative scheme to facilitate the application of our method to weakly correlated neurons.