Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior

Generative rules of Drosophila locomotor behavior as a candidate homology across phyla

The discovery of shared behavioral processes across phyla is a significant step in the establishment of a comparative study of behavior. We use immobility as an origin and reference for the measurement of fly locomotor behavior; speed, walking direction and trunk orientation as the degrees of freedom shaping this behavior; and cocaine as the parameter inducing progressive transitions in and out of immobility. We characterize and quantify the generative rules that shape Drosophila locomotor behavior, bringing about a gradual buildup of kinematic degrees of freedom during the transition from immobility to normal behavior, and the opposite narrowing down into immobility. Transitions into immobility unfold via sequential enhancement and then elimination of translation, curvature and finally rotation. Transitions out of immobility unfold by progressive addition of these degrees of freedom in the opposite order. The same generative rules have been found in vertebrate locomotor behavior in several contexts (pharmacological manipulations, ontogeny, social interactions) involving transitions in-and-out of immobility. Recent claims for deep homology between arthropod central complex and vertebrate basal ganglia provide an opportunity to examine whether the rules we report also share common descent. Our approach prompts the discovery of behavioral homologies, contributing to the elusive problem of behavioral evolution

Signatures of chaos in animal search patterns

One key objective of the emerging discipline of movement ecology is to link animal movement patternsto underlying biological processes, including those operating at the neurobiological level. Nonetheless,little is known about the physiological basis of animal movement patterns, and the underlying searchbehaviour. Here we demonstrate the hallmarks of chaotic dynamics in the movement patterns ofmud snails (Hydrobia ulvae) moving in controlled experimental conditions, observed in the temporaldynamics of turning behaviour. Chaotic temporal dynamics are known to occur in pacemaker neuronsin molluscs, but there have been no studies reporting on whether chaotic properties are manifest in themovement patterns of molluscs. Our results suggest that complex search patterns, like the Lévy walksmade by mud snails, can have their mechanistic origins in chaotic neuronal processes. This possibilitycalls for new research on the coupling between neurobiology and motor properties

Tracking nutrient decisions in Drosophila melanogaster

Animals integrate external sensory information and current metabolic needs to adapt their behavior in order to survive. Accordingly, many organisms can detect an internal nutritional imbalance and adjust their nutritional choices to restore homeostasis. Detailed quantitative analyses of nutrient-choice behaviors are needed to deepen our understanding of how neural circuits integrate internal state information and drive compensatory behavior when facing metabolic challenges. During this project, we developed an automated video tracking setup to characterize how metabolic and reproductive states interact to shape exploitation and exploration decisions taken by the adult fruit fly Drosophila melanogaster, to achieve nutritional homeostasis. We find that these two states have specific effects on the decisions to stop on and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient deprived flies focus on specific patches while satiated flies explore more globally. We provide few examples of how our paradigm could be used in the dissection of the genetic and neuronal pathways underlying nutrient decisions: First, we show that olfaction is not required for the compensatory high yeast feeding after amino acid deprivation, but that it mediates the efficient recognition of yeast as an appropriate food source in mated females. Second, we show that octopamine is required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Third, we show how gustation is required to sustain interest for protein-rich resources upon amino acid deprivation. Our results provide a quantitative description of how the fly changes behavioral decisions to achieve homeostatic nutrient balancing and provide a framework for future detailed mechanistic dissection of such decisions

The role of food odor in invertebrate foraging

Foraging for food is an integral part of animal survival. In small insects and inverte-brates, multisensory information and optimized locomotion strategies are used toeffectively forage in patchy and complex environments. Here, the importance ofolfactory cues for effective invertebrate foraging is discussed in detail. We reviewhow odors are used by foragers to move toward a likely food source and the recentmodels that describe this sensory-driven behavior. We argue that smell serves a sec-ond function by priming an organism for the efficient exploitation of food. Byappraising food odors, invertebrates can establish preferences and better adapt totheir ecological niches, thereby promoting survival. The smell of food pre-preparesthe gastrointestinal system and primes feeding motor programs for more effectiveingestion as well. Optimizing resource utilization affects longevity and reproductionas a result, leading to drastic changes in survival. We propose that models of foragingbehavior should include odor priming, and illustrate this with a simple toy modelbased on the marginal value theorem. Lastly, we discuss the novel techniques andassays in invertebrate research that could investigate the interactions between odorsensing and food intake. Overall, the sense of smell is indispensable for efficient for-aging and influences not only locomotion, but also organismal physiology, whichshould be reflected in behavioral modeling

Neuronal oscillations on an ultra-slow timescale: daily rhythms in electrical activity and gene expression in the mammalian master circadian clockwork

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Neuronal oscillations of the brain, such as those observed in the cortices and hippocampi of behaving animals and humans, span across wide frequency bands, from slow delta waves (0.1 Hz) to ultra-fast ripples (600 Hz). Here, we focus on ultra-slow neuronal oscillators in the hypothalamic suprachiasmatic nuclei (SCN), the master daily clock that operates on interlocking transcription-translation feedback loops to produce circadian rhythms in clock gene expression with a period of near 24 h (< 0.001 Hz). This intracellular molecular clock interacts with the cell's membrane through poorly understood mechanisms to drive the daily pattern in the electrical excitability of SCN neurons, exhibiting an up-state during the day and a down-state at night. In turn, the membrane activity feeds back to regulate the oscillatory activity of clock gene programs. In this review, we emphasise the circadian processes that drive daily electrical oscillations in SCN neurons, and highlight how mathematical modelling contributes to our increasing understanding of circadian rhythm generation, synchronisation and communication within this hypothalamic region and across other brain circuits.M.D.C.B is supported by the University ofExeter Medical School (UEMS). C.O.D’s work was partially supported bythe National Science Foundation under grant nos. DMS-1412877 and DMS-155237, and the U.S. Army Research Laboratory and the U.S. ArmyResearch Office under Grant No. W911NF-16-1-0584

Models for reinforcement learning and design of a soft robot inspired by Drosophila larvae

Designs for robots are often inspired by animals, as they are designed mimicking animals’ mechanics, motions, behaviours and learning. The Drosophila, known as the fruit fly, is a well-studied model animal. In this thesis, the Drosophila larva is studied and the results are applied to robots. More specifically: a part of the Drosophila larva’s neural circuit for operant learning is modelled, based on which a synaptic plasticity model and a neural circuit model for operant learning, as well as a dynamic neural network for robot reinforcement learning, are developed; then Drosophila larva’s motor system for locomotion is studied, and based on it a soft robot system is designed. Operant learning is a concept similar to reinforcement learning in computer science, i.e. learning by reward or punishment for behaviour. Experiments have shown that a wide range of animals is capable of operant learning, including animal with only a few neurons, such as Drosophila. The fact implies that operant learning can establish without a large number of neurons. With it as an assumption, the structure and dynamics of synapses are investigated, and a synaptic plasticity model is proposed. The model includes nonlinear dynamics of synapses, especially receptor trafficking which affects synaptic strength. Tests of this model show it can enable operant learning at the neuron level and apply to a broad range of NNs, including feedforward, recurrent and spiking NNs. The mushroom body is a learning centre of the insect brain known and modelled for associative learning, but not yet for operant learning. To investigate whether it participates in operant learning, Drosophila larvae are studied with a transgenic tool by my collaborators. Based on the experiment and the results, a mushroom body model capable of operant learning is modelled. The proposed neural circuit model can reproduce the operant learning of the turning behaviour of Drosophila larvae. Then the synaptic plasticity model is simplified for robot learning. With the simplified model, a recurrent neural network with internal neural dynamics can learn to control a planar bipedal robot in a benchmark reinforcement learning task which is called bipedal walker by OpenAI. Benefiting efficiency in parameter space exploration instead of action space exploration, it is the first known solution to the task with reinforcement learning approaches. Although existing pneumatic soft robots can have multiple muscles embedded in a component, it is far less than the muscles in the Drosophila larva, which are well-organised in a tiny space. A soft robot system is developed based on the muscle pattern of the Drosophila larva, to explore the possibility to embed a high density of muscles in a limited space. Three versions of the body wall with pneumatic muscles mimicking the muscle pattern are designed. A pneumatic control system and embedded control system are also developed for controlling the robot. With a bioinspired body wall will a large number of muscles, the robot performs lifelike motions in experiments

Nature and source of animal spontaneous behaviors: Insights from psychobehavioral development and neuronal population dynamics in mice

Awake animals switch between different behavioral states irregularly even in a homogenous and steady environment, especially obvious outside from any behavioral task when they are free to voluntarily behave. These irregular but structured patterns have been taken as a representation of internal states such as emotion, and are believed to represent underlying background brain activity and its dynamics. To date, the nature and source of animal spontaneous behaviors remain as a major conceptual challenge to academia, due to the lack of approaches to systematically and quantitatively examine this fundamental process. To achieve insights about the neural substrate of animal spontaneous behaviors, the research was conducted in two directions: (i) To interpret previously challenging and inconclusive behavioral development by re-evaluating spontaneous behaviors that represent emotionality, centered on the study of PTSD (post-traumatic stress disorder)-like internal psychological development in laboratory mice; (ii) To demonstrate a driving and control principle explaining fine-scale and global observations of neuronal and behavioral dynamics in spontaneously behaving mice, by two-photon calcium imaging of neuronal populations across cerebral cortical layers and areas. The results from these investigations provide the first system-level view of experimentally disentangled components, processes, and determinants explaining the nature and source of animal spontaneous behaviors.Okinawa Institute of Science and Technology Graduate Universit

Bursty behavioral dynamics of activity and sleep

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física Teórica. Fecha lectura: 22 de enero de 2016Una distinguida característica de la vida humana y animal son las constantes alternancias entre periodos activos dedicados a diferentes comportamientos, y periodos de inactividad dedicados a descanso y recuperación. El patrón temporal de actividad e inactividad está lleno de información de los procesos que controlan y regulan las transiciones entre estados. Tradicionalmente se había considerado que estos patrones temporales seguirían una estadística ‘normal’ como tantos otros fenómenos naturales. Pero durante las dos últimas décadas se ha ido descubriendo que muchos comportamientos, tanto en humanos como en otros animales, siguen una dinámica temporal en ráfagas y ‘sin escala’. La dinámica rafagosa se caracterizada por tener un patrón temporal donde periodos de muchos eventos cortos y frecuentes vienen separados por periodos largos de poca o ninguna actividad. La dinámica rafagosa es más irregular que la dinámica aleatoria, y es por lo tanto, más difícil de predecir. El objetivo de esta tesis doctoral ha sido estudiar la dinámica rafagosa del comportamiento espontáneo de actividad y sueño. La temática principal ha sido caracterizar la dinámica rafagosa en animales ‘simples’ y genéticamente maleables, para establecer resultados de base sobre los que construir y explorar el control neuronal subyacente de la dinámica rafagosa. La tesis consiste de tres partes principales, en las cuales hemos estudiado la dinámica de la actividad en la mosca de la fruta Drosophila melanogaster, la dinámica de sueño-vigilia en el pez cebra Danio rerio y en humanos, y estudiado las propiedades neuronales de la corriente “marcapasos” Ih que controla actividad espontánea rítmica y afecta a la rafagosidad. En Drosophila caracterizamos la dinámica locomotora y encontramos que es rafagosa. Después probamos experimentalmente una hipótesis sobre el origen de ráfagas, y descubrimos que circuitos de toma de decisiones afectan a la dinámica del comportamiento en ráfagas. Posteriormente, caracterizamos la dinámica de sueño y vigilia en el pez cebra a diferentes edades a lo largo de su vida y lo comparamos con el desarrollo de la dinámica de sueño-vigilia en humanos a diferentes edades. Encontramos que la fragmentación de la vigilia disminuye, a medida que los episodios de vigilia y vigilia nocturna total aumentan con la edad tanto en el pez cebra como en humanos. La dinámica de vigilia mostró ser altamente rafagosa, mientras que la dinámica de sueño tenía una estructura temporal más compleja de lo que ha sido predominantemente descrito. El desarrollo de la dinámica de sueño-vigilia es muy similar en las dos especies; lo que contribuye a establecer al pez cebra como un organismo modelo valioso para futuros estudios de la dinámica y regulación del sueño y de la vigilia. Finalmente, estudiamos la corriente Ih en Drosophila dado que la mutación nula del DmIh da lugar a alteraciones en el patrón de sueño-vigilia en las moscas adultas. La mutación también produce un fenotipo locomotor y de toma de decisiones en larvas, las cuales tienen un sistema nervioso más simple y una unión neuromuscular altamente caracterizada e idónea para la electrofisiología. Hallamos que el fenotipo locomotor larvario se debía a la motoneurona, que tenía una excitabilidad disminuida y una respuesta reducida a estímulos dinámicos. Los animales modelo con herramientas genéticas sofisticadas como la mosca de la fruta y el pez cebra han, por lo tanto, sido mostrados como animales valiosos para caracterizar y explorar el control y la regulación del comportamiento en ráfagas.alternations between active periods dedicated to different behaviors, and periods of inactivity dedicated to rest and recuperation. The temporal pattern of active and inactive periods is rich with information on the processes that govern and regulate the transitions among behavioral states. Traditionally these temporal patterns had been believed to follow ’normal’ statistics like so many other natural phenomena, but during the last two decades it has increasingly been discovered that many behaviors in both humans and other animals are instead governed by ’scale-free’ bursty temporal dynamics. Bursty dynamics are characterized by having a temporal pattern where periods of many short and frequent events are separated by long periods of little or no activity. Bursty dynamics are thus more irregular than random dynamics and harder to predict. The aim of this doctoral thesis has been to study the bursty behavioral dynamics of spontaneous activity and sleep. The main theme has been to characterize the bursty dynamics in genetically tractable ’simpler’ model organisms, to establish baseline results to build upon and to probe the underlying neuronal control of burstiness. The thesis consists of three main parts, in which we have studied activity dynamics in the fruit fly Drosophila melanogaster, sleep-wake dynamics in the zebrafish Danio rerio and in humans, and studied the properties of the neuronal Ih “pacemaker” current which controls spontaneous rhythmic activity and affects burstiness. In Drosophila we characterized the locomotor activity dynamics and found that it is bursty. Subsequently we experimentally tested a hypothesis on the origin of bursts, and found that decision-making circuits affect the bursty behavioral dynamics. We next characterized the sleep-wake dynamics in the zebrafish at different ages across the lifespan and compared it to the development of sleep-wake dynamics in humans at different ages. We found that nightly wake increases as wake durations become longer and less fragmented with age in both zebrafish and humans. Wake dynamics were found to be highly bursty, while sleep dynamics were found to have a more complex temporal dynamics than predominantly described. The highly similar development of sleep-wake cycles in both species contributes to establishing zebrafish as a valuable model organism for further studies of sleep-wake dynamics and regulation. Finally, we studied the Ih current as the DmIh null mutation gives rise to alterations of the sleep-wake pattern in adult fruit flies. The mutation also produces a locomotor and decision-making phenotype in larvae, which have a much simpler nervous system and a well characterized neuromuscular junction suitable for electrophysiology. We found that the larval locomotor phenotype was due to the motoneurons, which exhibited a decreased excitability and reduced responsiveness to dynamic stimuli. Model organisms with sophisticated genetical tools like the fruit fly and the zebrafish have thus been shown to be highly valuable animal models for characterizing and probing the control and regulation of behavioral burstiness