Success rates, trajectory lengths, and deviation from the optimal path.

<p>(A) Success rates of reactive search strategies, different colors indicate different strategies (legend in C), grouping indicates different stimulation doses (three doses plus no stimulation). (B) Success rates of cognitive searching with infotaxis (three doses plus no stimulation). (C) Trajectory lengths of reactive search strategies, different colors indicate different strategies, grouping indicates different stimulation doses. (D) Trajectory lengths of cognitive searching with infotaxis (three doses plus no stimulation). (E) Schematic drawing to explain the measure: the average of horizontal deviations (Xi) from trajectory to shortest path between start and source. (F) Deviation from the optimal path () for reactive searching, different colors indicate different reactive strategies for the three groups of pheromone doses. (G) Deviation from the optimal path () for cognitive searching (three doses plus no stimulation). Box plots are explained in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003861#s4" target="_blank">Methods</a>, part 2, the numbers indicate mean standard deviation.</p

Experimental robotic trials.

<p>Experimental robotic trials.</p

Reactive search strategies and their biological motivation.

<p>(A) MGC recordings for pheromone stimulation: spike times for seven trials of one neuron and the corresponding average firing rate over time (Peri-Stimulus-Time-Histogram): inhibition separates the On from the Off response which smoothly decreases to baseline firing (Bl). (B) Analysis of MGC recordings of multiphasic neurons: Calculating the regularity () and reliability () over time exhibits an Off phase, whereas Off and baseline firing show uniformly low synchrony values (). Dotted black lines represent single neuron trials, the red line gives the averages. (C) Analysis of MGC recordings of monophasic neurons: neither synchrony nor regularity nor reliability over time exhibit any Off phase. Dotted black lines represent single neuron trials, the blue line gives the averages. (Right side: za, ze, sp) Schematic representation of the corresponding movement sequences: Bl spiraling, On upwind surge, and Off zigzagging (if considered) which are combined into three search strategies, <i>sp</i>, <i>za</i>, and <i>ze</i>.</p

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Sensory systems, both in the living and in machines, have to be optimized with respect to their environmental conditions. The pheromone subsystem of the olfactory system of moths is a particularly well-defined example in which rapid variations of odor content in turbulent plumes require fast, concentration-invariant neural representations. It is not clear how cellular and network mechanisms in the moth antennal lobe contribute to coding efficiency. Using computational modeling, we show that intrinsic potassium currents (IA and ISK) in projection neurons may combine with extrinsic inhibition from local interneurons to implement a dual latency code for both pheromone identity and intensity. The mean latency reflects stimulus intensity, whereas latency differences carry concentration-invariant information about stimulus identity. In accordance with physiological results, the projection neurons exhibit a multiphasic response of inhibitionexcitationinhibition. Together with synaptic inhibition, intrinsic currents IA and ISK account for the first and second inhibitory phases and contribute to a rapid encoding of pheromone information. The first inhibition plays the role of a reset to limit variability in the time to first spike. The second inhibition prevents responses of excessive duration to allow tracking of intermittent stimuli

Reactive search trajectories.

<p>(A) Examples of <i>sp</i> search trajectories (spirals only, i.e., no Off), medium dose. For a better visualization single paths are plotted in distinct colors (cyan and light blue on top of mostly blue trajectories). The dots on the trajectories indicate pheromone detections. The black dashed line indicates the plume contour (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003861#s4" target="_blank">Methods</a>). (B) Examples of search trajectories including Off zigzagging, medium dose. Red, yellow and pink trajectories use arithmetic spirals (<i>za</i>), bluish trajectories originate from assuming exponential spirals (<i>ze</i>). Identical conventions as in (A). (C and D) Track-angle histogram of <i>sp</i> and <i>za</i> trajectories, respectively, different colors indicate different pheromone doses. (E) Total number of turns for different stimulations, different colors indicate different reactive strategies for the three groups of pheromone doses, identical conventions as in Fig. 3.</p

Experimental set-up of the cyborg's search task.

<p>(A) Schematic general set-up: the cyborg starts 2 m from the pheromone source in a 2.54 m region. A fan provides a wind blowing from the top (towards the cyborg). (B) Photo of our cyborg: a Khepera III robot with a moth fixed in a styrofoam roll. Zoom-in 1: top of the styrofoam roll with the insect's head and the two antennae on the outside. Zoom-in 2: one antenna enters the tip of a glass electrode. Photographs by H. Raguet — INRIA.</p

Cognitive search trajectories obtained using infotaxis.

<p>(A) Example <i>it</i> trajectories for no stimulation (green, left), minimum (dark green, middle) and medium (cyan, right) stimulation doses. The dots on the trajectories indicate pheromone detections. The black dashed line indicates the plume contour (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003861#s4" target="_blank">Methods</a>). (B) Total number of turns in <i>it</i> trajectories for different stimulations. Identical conventions as in Fig. 3. (C) Track-angle histograms of <i>it</i> trajectories, different colors indicate different doses. (D) Total number of detections measured during reactive (<i>sp</i>, <i>za</i>, <i>ze</i>) and cognitive (<i>it</i>) searching using three stimulation doses and no stimulation. Identical conventions as in Fig. 3.</p

Crossvalidation performance as a function of the number of chosen features.

<p>The boxplots represent the distribution of observed fraction of correct classification for all feature choices of <i>n</i> features, with <i>n</i> ranging from 1 feature (top line) to all 16 features (bottom), see labels in the middle column. The colour bars indicate the performance for the best, worst, top10 (see main text for definition) and median feature choice. Panel A and C show the performances for classifying all three categories of good, intermediate and bad for data set 1 (A) and data set 2 (C). Panels B and D show the results for classifying “good” against the combined category of “intermediate or bad”.</p

Human and machine judgements on data set 2.^a

a<p>Correlation between the prediction vectors (bad = −1, intermediate = 0, good = 1).</p>b<p>The last column and row show the correlation to the result of feature selection and training on data set 1 and then predicting data set 2 with all members of the top10 group of size 13 (the one performing best, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080838#pone-0080838-g008" target="_blank">Figure 8</a>).</p

Overview of the observed distributions of feature values for data set 1.

<p>The value distributions are shown separately for recordings that were classified as good (red), intermediate (green) or bad (blue) by expert 1. We have compared the distributions with Kolmogorov-Smirnov tests and found that many but not all distributions differ significantly on significance level α = 0.05 (one star) or α = 0.01 (two stars). We note that the distributions between intermediate and bad recordings rarely differ significantly but often both do differ from the good recordings.</p