Machine learning for automatic prediction of the quality of electrophysiological recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters

A biophysical model of the early olfactory system of honeybees

Experimental measurements often can only provide limited data from an animal’s sensory system. In addition, they exhibit large trial-to-trial and animal-to-animal variability. These limitations pose challenges to building mathematical models intended to make biologically relevant predictions. Here, we present a mathematical model of the early olfactory system of honeybees aiming to overcome these limitations. The model generates olfactory response patterns which conform to the statistics derived from experimental data for a variety of their properties. This allows considering the full dimensionality of the sensory input space as well as avoiding overfitting the underlying data sets. Several known biological mechanisms, including processes of chemical binding and activation of receptors, and spike generation and transmission in the antennal lobe network, are incorporated in the model at a minimal level. It can therefore be used to study how experimentally observed phenomena are shaped by these underlying biophysical processes. We verified that our model can replicate some key experimental findings that were not used when building it. Given appropriate data, our model can be generalized to the early olfactory systems of other insects. It hence provides a possible framework for future numerical and analytical studies of olfactory processing in insects

Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons

Elemental and configural olfactory coding by antennal lobe neurons of the honeybee (Apis mellifera)

When smelling an odorant mixture, olfactory systems can be analytical (i.e. extract information about the mixture elements) or synthetic (i.e. creating a configural percept of the mixture). Here, we studied elemental and configural mixture coding in olfactory neurons of the honeybee antennal lobe, local neurons in particular. We conducted intracellular recordings and stimulated with monomolecular odorants and their coherent or incoherent binary mixtures to reproduce a temporally dynamic environment. We found that about half of the neurons responded as ‘elemental neurons’, i.e. responses evoked by mixtures reflected the underlying feature information from one of the components. The other half responded as ‘configural neurons’, i.e. responses to mixtures were clearly different from responses to their single components. Elemental neurons divided in late responders (above 60 ms) and early responder neurons (below 60 ms), whereas responses of configural coding neurons concentrated in-between these divisions. Latencies of neurons with configural responses express a tendency to be faster for coherent stimuli which implies employment in different processing circuits

Signal extraction from movies of honeybee brain activity: the ImageBee plugin for KNIME

BACKGROUND: In the antennal lobe, a dedicated olfactory center of the honeybee brain, odours are encoded as activity patterns of coding units, the so-called glomeruli. Optical imaging with calcium-sensitive dyes allows us to record these activity patterns and to gain insight into olfactory information processing in the brain. METHOD: We introduce ImageBee, a plugin for the data analysis platform KNIME. ImageBee provides a variety of tools for processing optical imaging data. The main algorithm behind ImageBee is a matrix factorisation approach. Motivated by a data-specific, non-negative mixture model, the algorithm aims to select the generating extreme vectors of a convex cone that contains the data. It approximates the movie matrix by non-negative combinations of the extreme vectors. These correspond to pure glomerular signals that are not mixed with neighbour signals. RESULTS: Evaluation shows that the proposed algorithm can identify the relevant biological signals on imaging data from the honeybee AL, as well as it can recover implanted source signals from artificial data. CONCLUSIONS: ImageBee enables automated data processing and visualisation for optical imaging data from the insect AL. The modular implementation for KNIME offers a flexible platform for data analysis projects, where modules can be rearranged or added depending on the particular application. AVAILABILITY: ImageBee can be installed via the KNIME update service. Installation instructions are available at http://tech.knime.org/imagebee-analysing-imaging-data-from-the-honeybee-brain

Queen mandibular pheromone: questions that remain to be resolved

The discovery of ‘queen substance’, and the subsequent identification and synthesis of keycomponents of queen mandibular pheromone, has been of significant importance to beekeepers and to thebeekeeping industry. Fifty years on, there is greater appreciation of the importance and complexity of queenpheromones, but many mysteries remain about the mechanisms through which pheromones operate. Thediscovery of sex pheromone communication in moths occurred within the same time period, but in this case,intense pressure to find better means of pest management resulted in a remarkable focusing of research activityon understanding pheromone detection mechanisms and the central processing of pheromone signals in themoth. We can benefit from this work and here, studies on moths are used to highlight some of the gaps in ourknowledge of pheromone communication in bees. A better understanding of pheromone communication inhoney bees promises improved strategies for the successful management of these extraordinary animals

A multimodal approach for tracing lateralization along the olfactory pathway in the honeybee through electrophysiological recordings, morpho-functional imaging, and behavioural studies

Recent studies have revealed asymmetries between the left and right sides of the brain in invertebrate species. Here we present a review of a series of recent studies from our labs, aimed at tracing asymmetries at different stages along the honeybee's (Apis mellifera) olfactory pathway. These include estimates of the number of sensilla present on the two antennae, obtained by scanning electron microscopy, as well as electroantennography recordings of the left and right antennal responses to odorants. We describe investigative studies of the antennal lobes, where multi-photon microscopy is used to search for possible morphological asymmetries between the two brain sides. Moreover, we report on recently published results obtained by two-photon calcium imaging for functional mapping of the antennal lobe aimed at comparing patterns of activity evoked by different odours. Finally, possible links to the results of behavioural tests, measuring asymmetries in single-sided olfactory memory recall, are discussed.Comment: 28 pages, 8 figure

An Experimental Biomimetic Platform for Artificial Olfaction

Artificial olfactory systems have been studied for the last two decades mainly from the point of view of the features of olfactory neuron receptor fields. Other fundamental olfaction properties have only been episodically considered in artificial systems. As a result, current artificial olfactory systems are mostly intended as instruments and are of poor benefit for biologists who may need tools to model and test olfactory models. Herewith, we show how a simple experimental approach can be used to account for several phenomena observed in olfaction

Exploring miniature insect brains using micro-CT scanning techniques

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Friends and Foes from an Ant Brain's Point of View – Neuronal Correlates of Colony Odors in a Social Insect

Background: Successful cooperation depends on reliable identification of friends and foes. Social insects discriminate colony members (nestmates/friends) from foreign workers (non-nestmates/foes) by colony-specific, multi-component colony odors. Traditionally, complex processing in the brain has been regarded as crucial for colony recognition. Odor information is represented as spatial patterns of activity and processed in the primary olfactory neuropile, the antennal lobe (AL) of insects, which is analogous to the vertebrate olfactory bulb. Correlative evidence indicates that the spatial activity patterns reflect odor-quality, i.e., how an odor is perceived. For colony odors, alternatively, a sensory filter in the peripheral nervous system was suggested, causing specific anosmia to nestmate colony odors. Here, we investigate neuronal correlates of colony odors in the brain of a social insect to directly test whether they are anosmic to nestmate colony odors and whether spatial activity patterns in the AL can predict how odor qualities like ‘‘friend’’ and ‘‘foe’’ are attributed to colony odors. Methodology/Principal Findings: Using ant dummies that mimic natural conditions, we presented colony odors and investigated their neuronal representation in the ant Camponotus floridanus. Nestmate and non-nestmate colony odors elicited neuronal activity: In the periphery, we recorded sensory responses of olfactory receptor neurons (electroantennography), and in the brain, we measured colony odor specific spatial activity patterns in the AL (calcium imaging). Surprisingly, upon repeated stimulation with the same colony odor, spatial activity patterns were variable, and as variable as activity patterns elicited by different colony odors. Conclusions: Ants are not anosmic to nestmate colony odors. However, spatial activity patterns in the AL alone do not provide sufficient information for colony odor discrimination and this finding challenges the current notion of how odor quality is coded. Our result illustrates the enormous challenge for the nervous system to classify multi-component odors and indicates that other neuronal parameters, e.g., precise timing of neuronal activity, are likely necessary for attribution of odor quality to multi-component odors