Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data

Design principles of hair-like structures as biological machines

Hair-like structures are prevalent throughout biology and frequently act to sense or alter interactions with an organism's environment. The overall shape of a hair is simple: a long, filamentous object that protrudes from the surface of an organism. This basic design, however, can confer a wide range of functions, owing largely to the flexibility and large surface area that it usually possesses. From this simple structural basis, small changes in geometry, such as diameter, curvature and inter-hair spacing, can have considerable effects on mechanical properties, allowing functions such as mechanosensing, attachment, movement and protection. Here, we explore how passive features of hair-like structures, both individually and within arrays, enable diverse functions across biology. Understanding the relationships between form and function can provide biologists with an appreciation for the constraints and possibilities on hair-like structures. Additionally, such structures have already been used in biomimetic engineering with applications in sensing, water capture and adhesion. By examining hairs as a functional mechanical unit, geometry and arrangement can be rationally designed to generate new engineering devices and ideas

Effect of temperature and light intensity on the representation of motion information in the fly's visual system

To comprehend how the brain performs efficient computation, it is important to understand the way sensory information is represented in the nervous system. Under natural conditions, sensory signals have to be processed with sufficient accuracy under functional and resources constraints. Here I use motion vision in the fly Calliphora vicina to study the influence of two behaviorally relevant environmental properties - temperature and light intensity - on the representation of motion information in the responses of the neuron H1. The goal was to quantify how these environmental properties affect the response variability, information content, coding efficiency and temporal scale. I show that the firing precision is determined largely by the light intensity rather than by temperature. Moreover, a better firing precision barely improves the information rate, which closely follows the mean firing rate. Altogether, my results suggest that the robustness of the motion information processing against temperature variations depends on the quality of the input signal. Furthermore, flies seem to use the input signal-to-noise ratio to improve the information rate and reduce the time-scale of the response simultaneously, by increasing the mean firing rate, rather than the firing precision

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Hearing and acoustic interaction in mosquitoes

Johnston, who discovered the mosquito auditory organ at the base of the antennae 150 years ago, speculated that audition was involved in mating behaviour. Indeed, the Johnston’s organ (JO) is now known to detect the whine of flying mosquitoes. Analysis of sound recordings of flight tones from tethered, flying, mosquitoes revealed that opposite-sex pairs, when within their acoustic near-fields, attempt to frequency-match the harmonic components of their flighttones. Same-sex pairs actively avoid frequency-matching. Mosquitoes of the species Toxorhynchites brevipalpis, where the flight-tone frequencies of males and females are similar, attempt to match the fundamental frequency of their flight-tones. Haemophilic, vector-carrying mosquitoes Culex quinquefasciatus and Anopheles gambiae ss, where the fundamental frequency of the male flight tone is about 1.5 times that of the female, frequency-match harmonic components of their flight tones. Usually the male’s 1st harmonic with the 2nd harmonic of the female flight-tone. In Burkina Faso, where two morphologically similar molecular forms aggregate in the same swarms but rarely hybridise, frequency-matching of flight-tones may perform a pre-mating barrier and a form of subspecies recognition. We discovered that frequency-matching occurred significantly more frequently between same-form male-female pairs of flying, tethered mosquitoes, than when each member of the pair was of a different molecular form. The bandwidth and tuning of sound-evoked flagellum vibrations and the JO’s electrical responses to this mechanical input were measured using laser interferometry and extracellular electrodes, respectively. For the first time we showed that distortion products, recorded from the flagellum and JO, could provide the neural basis for frequency-matching at frequencies beyond the range of the JO’s electrical responses. We also discovered that spontaneous oscillations of the antennae are produced by physiologically-sensitive mosquitoes. Through temperature-control and injection of pharmacological agents into the JO, evidence is presented advocating dynein as the molecular motor responsible for powering these oscillations

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Olfactory learning in Drosophila

Animals are able to form associative memories and benefit from past experience. In classical conditioning an animal is trained to associate an initially neutral stimulus by pairing it with a stimulus that triggers an innate response. The neutral stimulus is commonly referred to as conditioned stimulus (CS) and the reinforcing stimulus as unconditioned stimulus (US). The underlying neuronal mechanisms and structures are an intensely investigated topic. The fruit fly Drosophila melanogaster is a prime model animal to investigate the mechanisms of learning. In this thesis we propose fundamental circuit motifs that explain aspects of aversive olfactory learning as it is observed in the fruit fly. Changing parameters of the learning paradigm affects the behavioral outcome in different ways. The relative timing between CS and US affects the hedonic value of the CS. Reversing the order changes the behavioral response from conditioned avoidance to conditioned approach. We propose a timing-dependent biochemical reaction cascade, which can account for this phenomenon. In addition to form odor-specific memories, flies are able to associate a specific odor intensity. In aversive olfactory conditioning they show less avoidance to lower and higher intensities of the same odor. However the layout of the first two olfactory processing layers does not support this kind of learning due to a nested representation of odor intensity. We propose a basic circuit motif that transforms the nested monotonic intensity representation to a non-monotonic representation that supports intensity specific learning. Flies are able to bridge a stimulus free interval between CS and US to form an association. It is unclear so far where the stimulus trace of the CS is represented in the fly's nervous system. We analyze recordings from the first three layers of olfactory processing with an advanced machine learning approach. We argue that third order neurons are likely to harbor the stimulus trace