Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. We find that NSIs improve mixture ratio detection and plume structure sensing and do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. The best performance is achieved when both mechanisms work in synergy. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination

Learning selective top-down control enhances performance in a visual categorization task.

We model the putative neuronal and synaptic mechanisms involved in learning a visual categorization task, taking inspiration from single-cell recordings in inferior temporal cortex (ITC). Our working hypothesis is that learning the categorization task involves both bottom-up, ITC to prefrontal cortex (PFC), and top-down (PFC to ITC) synaptic plasticity and that the latter enhances the selectivity of the ITC neurons encoding the task-relevant features of the stimuli, thereby improving the signal-to-noise ratio. We test this hypothesis by modeling both areas and their connections with spiking neurons and plastic synapses, ITC acting as a feature-selective layer and PFC as a category coding layer. This minimal model gives interesting clues as to properties and function of the selective feedback signal from PFC to ITC that help solving a categorization task. In particular, we show that, when the stimuli are very noisy because of a large number of nonrelevant features, the feedback structure helps getting better categorization performance and decreasing the reaction time. It also affects the speed and stability of the learning process and sharpens tuning curves of ITC neurons. Furthermore, the model predicts a modulation of neural activities during error trials, by which the differential selectivity of ITC neurons to task-relevant and task-irrelevant features diminishes or is even reversed, and modulations in the time course of neural activities that appear when, after learning, corrupted versions of the stimuli are input to the network

Multiple choice neurodynamical model of the uncertain option task

The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.This work was supported by MINECO (PSI2013-42091-P), Agència de Gestio d'Ajuts Universitaris i de Recerca (AGAUR-2014SGR856), European Research Council (ERC) Advanced Grant DYSTRUCTURE (n. 295129), FlagERA ChampMouse PCIN-2015- 127 and FET-Flagship HPB-SGA1 (720270). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Spatial distribution of non-linear interdependency measures for focal hemisphere identification in epileptic patients from interictal intracranial EEG

We study performance, stability and spatial distribution of three previously proposed [1,2] non-linear measures of interdependency between time series (named H, M and S) used to classify, from interictal recordings, the epileptogenic hemisphere in patients with drug-resistant mesial temporal lobe epilepsy (MTLE). Two electrodes penetrate the hippocampal region in the two hemispheres and include 10 recording contacts each. We consider only data recorded during interictal periods [3]. All measures are introduced through the reconstruction of a "state-space" for the recorded signals, using the widely adopted "embedding" approach, and quantify in different ways the average distance between neighbours in phase space for one signal and the distance between the corresponding equal-time partners in the other signal. One hemisphere is classified as focal if the average between-contacts interdependency value is significantly greater than the one for the other hemisphere. We investigate the dependence of the interdependency measures and associated performances on the inter-electrode distance, on the relevant parameters and non-stationarities across interictal periods. Two of the three measures (H and M) provide good and similar classification performances, as well as similar spatial distributions. For M, ten cases are correctly classified, one case is incorrectly classified and for four cases, M values are statistically indistinguishable for the two hemispheres. For the correctly classified cases, M shows long-range between-contacts interdependencies for the focal hemisphere (see Figure 1). We also show how interdependencies vary inside one interictal period and between different interictal periods. The role of the parameters entering the analysis is systematically studied to provide heuristic criteria for their choice. Two of the studied nonlinear measures are found to be adequate for the classification of the focal hemisphere. The observed long-range interdependency for focal hemispheres is consistent with the expected propensity of pathological nervous tissue to be entrained in paroxysmal synchronous activity. We suggest that the observed non-stationarity of interdependencies across interictal periods could be used to improve the classification performance

Attractors and their basins represented in the <i>ν</i>_<i>L</i>, <i>ν</i>_<i>R</i> plane with mean dynamics.

<p>A, Top: attractors landscape for Δ<i>λ</i> = 0. Attractors (dots) and their basins (dashed lines) during stimulus condition and sure target condition. Bottom: Traces show average dynamics of correct, error and sure choices (depending on the final point reached by the trace). The color of the trace represents time. Relevant events during the trial are marked by symbols as explained in the figure. Attractors and basins as in top panels are shown for reference (dots of sure target condition replaced by crosses for clarity). B: Attractors landscape and dynamics as in A, for Δ<i>λ</i> = 7. C: Attractors landscape and dynamics as in A, for Δ<i>λ</i> = 28. Note that the traces corresponding to left and sure choices are overlapped but left trials remain near the gray decision memory attractor.</p