Biology subjects

Friedrich, R. W.

Publications and source records attributed to Friedrich, R. W..

2 recordsLinked to original sources

bioRxiv2018

Rapid olfactory discrimination learning in adult zebrafish

The zebrafish is a model organism to study olfactory information processing but efficient behavioral procedures to analyze olfactory discrimination and memory are lacking. We devised an automated odor discrimination task for adult zebrafish based on olfactory conditioning of feeding behavior. Presentation of a conditioned odor (CS+), but not a neutral odor (CS-), was followed by food delivery at a specific location. Fish developed differential behavioral responses to CS+ and CS- within a few trials even when odors were similar. The behavioral response to the CS+ was complex and included components reminiscent of food search such as increased swimming speed and water surface sampling. Appetitive behavior was therefore quantified by a composite score that combined measurements of multiple behavioral parameters. Discrimination behavior was robust in different strains and learned preferences could overcome innate odor preferences. These results confirm that zebrafish can rapidly learn to make fine odor discriminations. The procedure is efficient and provides novel opportunities to dissect the neural mechanisms underlying olfactory discrimination and memory.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗