Insect circadian clock outputs

Insects display an impressive variety of daily rhythms, which are most evident in their behaviour. Circadian timekeeping systems that generate these daily rhythms of physiology and behaviour all involve three interacting elements: the timekeeper itself (i.e. the clock), inputs to the clock through which it entrains and otherwise responds to environmental cues such as light and temperature, and outputs from the clock through which it imposes daily rhythms on various physiological and behavioural parameters. In insects, as in other animals, cellular clocks are embodied in clock neurons capable of sustained autonomous circadian rhythmicity, and those clock neurons are organized into clock circuits. Drosophila flies spend their entire lives in small areas near the ground, and use their circadian brain clock to regulate daily rhythms of rest and activity, so as to organize their behaviour appropriately to the daily rhythms of their local environment. Migratory locusts and butterflies, on the other hand, spend substantial portions of their lives high up in the air migrating long distances (sometimes thousands of miles) and use their circadian brain clocks to provide time-compensation to their sun-compass navigational systems. Interestingly, however, there appear to be substantial similarities in the cellular and network mechanisms that underlie circadian outputs in all insects.     

The why,what, where,when and how of goal-directed choice: neuronal and computational principles

The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where and When can this be obtained, and How do I get it?'' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.     

The anatomy of choice: dopamine and decision-making

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.     

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate–the visual looming–of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.     

Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following

The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world.However,current stud-ies have not been able to reveal the mechanism of drivers'natural driving behaviors.Therefore,this thesis starts from the perspective of cognitive decision-making in the human brain,which is inspired by the regulation of dopamine feedback in the basal ganglia,and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the environment.In this thesis,first,a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism;second,the above mechanism was transformed into a reinforcement Q-learning model,so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following;finally,the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.     

Insect–machine hybrid system for understanding and evaluating sensory-motor control by sex pheromone in Bombyx mori

To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an ‘insect–machine hybrid system’ is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect–machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions.     

Composite collective decision-making

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive group-level decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms.     

Learning and coding of concepts in neural networks

Our environment consists of virtually an infinite number of scenarios in which we have to function. In order to respond properly to an incoming stimulus, the brain has first to analyze it, and to find out the basic familiar elements that are part of it. In other words, by using a library which contains a relatively small number of basic concepts, the brain analyzes the multitude of incoming events. Some of those basic concepts are innate, but many of them must be learned, in order to accommodate for the arbitrary environment around us. A classifying box is defined as the neural network that finds out the familiar concepts that are present in an incoming stimulus. Models for classifying boxes are introduced, and possible mechanisms by which they may establish their libraries of concepts are suggested, and then compared and evaluated by computer simulations.     

Potential disadvantages of using socially acquired information

The acquisition and use of socially acquired information is commonly assumed to be profitable. We challenge this assumption by exploring hypothetical scenarios where the use of such information either provides no benefit or can actually be costly. First, we show that the level of incompatibility between the acquisition of personal and socially acquired information will directly affect the extent to which the use of socially acquired information can be profitable. When these two sources of information cannot be acquired simultaneously, there may be no benefit to socially acquired information. Second, we assume that a solitary individual's behavioural decisions will be based on cues revealed by its own interactions with the environment. However, in many cases, for social animals the only socially acquired information available to individuals is the behavioural actions of others that expose their decisions, rather than the cues on which these decisions were based. We argue that in such a situation the use of socially acquired information can lead to informational cascades that sometimes result in sub-optimal behaviour. From this theory of informational cascades, we predict that when erroneous cascades are costly, individuals should pay attention only to socially generated cues and not behavioural decisions. We suggest three scenarios that might be examples of informational cascades in nature.     

A decision-making model based on a spiking neural circuit and synaptic plasticity

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.     

Dynamics of spiking neurons: between homogeneity and synchrony

Randomly connected networks of neurons driven by Poisson inputs are often assumed to produce “homogeneous” dynamics, characterized by largely independent firing and approximable by diffusion processes. At the same time, it is well known that such networks can fire synchronously. Between these two much studied scenarios lies a vastly complex dynamical landscape that is relatively unexplored. In this paper, we discuss a phenomenon which commonly manifests in these intermediate regimes, namely brief spurts of spiking activity which we call multiple firing events (MFE). These events do not depend on structured network architecture nor on structured input; they are an emergent property of the system. We came upon them in an earlier modeling paper, in which we discovered, through a careful benchmarking process, that MFEs are the single most important dynamical mechanism behind many of the V1 phenomena we were able to replicate. In this paper we explain in a simpler setting how MFEs come about, as well as their potential dynamic consequences. Although the mechanism underlying MFEs cannot easily be captured by current population dynamics models, this phenomena should not be ignored during analysis; there is a growing body of evidence that such collaborative activity may be a key towards unlocking the possible functional properties of many neuronal networks.     

Genetic analysis of the Drosophila ellipsoid body neuropil: organization and development of the central complex.

The central complex is an important center for higher-order brain function in insects. It is an intricate neuropil composed of four substructures. Each substructure contains repeated neuronal elements which are connected by processes such that topography is maintained. Although the neuronal architecture has been described in several insects and the behavioral role investigated in various experiments, the exact function of this neuropil has proven elusive. To describe the architecture of the central complex, we study 15 enhancer-trap lines that label various ellipsoid body neuron types. We find evidence for restriction of gene expression that is correlated with specific neuronal types: such correlations suggest functional classifications as well. We show that some enhancer-trap patterns reveal a single ellipsoid body neuron type, while others label multiple types. We describe the development of the ellipsoid body neuropil in wild-type animals and propose developmental mechanisms based on animals displaying structural mutations of this neuropil. The experiments performed here demonstrate the degree of resolution possible from the analysis of enhancer-trap lines and form a useful library of tools for future structure/function studies of the ellipsoid body.     

Collective Decision-Making in Homing Pigeons: Larger Flocks Take Longer to Decide but Do Not Make Better Decisions

Social animals routinely are challenged to make consensus decisions about movement directions and routes. However, the underlying mechanisms facilitating such decision-making processes are still poorly known. A prominent question is how group members participate in group decisions. We addressed this question by examining how flocks of homing pigeons (Columba livia) decide their homing direction. We released newly formed flocks varying in size and determined the time taken to choose a homing direction (decision-making period) and the accuracy of that choice. We found that the decision-making period increases exponentially with flock size, which is consistent with a participatory decision-making process. We additionally found that there is no effect of flock size on the accuracy of the decisions made, which does not match with current theory for democratic choices of flight directions. Our combined results are better explained by a participatory choice of leaders that subsequently undertake the flock directional decisions. However, this decision-making model would only entirely fit with our results if leaders were chosen based on traits other than their navigational experience. Our study provides rare empirical evidence elucidating decision-making processes in freely moving groups of animals.     

Peculiarities of Formation and Reversibility of Neurogenic Stress-Related Emotional Disorders in Rats

In experiments on rats, we modeled neurogenic stress-induced emotional disorders (stress was evoked by repetitive nociceptive stimulation) and studied their peculiarities within the stressory and post-stressory periods. In these animals, drastic changes in the brain electrical activity and emotional behavior gradually developed; such changes were manifested over a long time period after cessation of the stressory influences. Our experiments demonstrated that tight and stable interrelations among brain limbic structures and negative hypothalamic emotional centers are formed under conditions of prolonged action of emotional stress. This results in the development of a protracted state of negative emotional excitation. The hippocampus is considered one of the key limbic structures responsible for the development of stable pathological stress-related reactions of the brain. Within the post-stressory period, we observed dramatic worsening of the general functional state of the animals, which developed in a parallel manner with intensification of the activity of the negative emotiogenic brain system. It is probable that the existence of periods of unstable equilibrium between oppositely directed emotional reactions in the dynamics of stress and after cessation of stressory influences is a common rule. Such periods reflect peculiarities of rearrangements in the adaptive brain mechanisms under conditions of a stable change in the mode of brain functioning in one particular situation or another.     

Distinguishing social from nonsocial navigation in moving animal groups

Many animals, such as migrating shoals of fish, navigate in groups. Knowing the mechanisms involved in animal navigation is important when it comes to explaining navigation accuracy, dispersal patterns, population and evolutionary dynamics, and consequently, the design of conservation strategies. When navigating toward a common target, animals could interact socially by sharing available information directly or indirectly, or each individual could navigate by itself and aggregations may not disperse because all animals are moving toward the same target. Here we present an analysis technique that uses individual movement trajectories to determine the extent to which individuals in navigating groups interact socially, given knowledge of their target. The basic idea of our approach is that the movement directions of individuals arise from a combination of responses to the environment and to other individuals. We estimate the relative importance of these responses, distinguishing between social and nonsocial interactions. We develop and test our method, using simulated groups, and we demonstrate its applicability to empirical data in a case study on groups of guppies moving toward shelter in a tank. Our approach is generic and can be extended to different scenarios of animal group movement.     

Humans and insects decide in similar ways

Behavioral ecologists assume that animals use a motivational mechanism for decisions such as action selection and time allocation, allowing the maximization of their fitness. They consider both the proximate and ultimate causes of behavior in order to understand this type of decision-making in animals. Experimental psychologists and neuroeconomists also study how agents make decisions but they consider the proximate causes of the behavior. In the case of patch-leaving, motivation-based decision-making remains simple speculation. In contrast to other animals, human beings can assess and evaluate their own motivation by an introspection process. It is then possible to study the declared motivation of humans during decision-making and discuss the mechanism used as well as its evolutionary significance. In this study, we combine both the proximate and ultimate causes of behavior for a better understanding of the human decision-making process. We show for the first time ever that human subjects use a motivational mechanism similar to small insects such as parasitoids and bumblebees to decide when to leave a patch. This result is relevant for behavioral ecologists as it supports the biological realism of this mechanism. Humans seem to use a motivational mechanism of decision making known to be adaptive to a heterogeneously distributed resource. As hypothesized by Hutchinson et al. and Wilke and Todd, our results are consistent with the evolutionary shaping of decision making because hominoids were hunters and gatherers on food patches for more than two million years. We discuss the plausibility of a neural basis for the motivation mechanism highlighted here, bridging the gap between behavioral ecology and neuroeconomy. Thus, both the motivational mechanism observed here and the neuroeconomy findings are most likely adaptations that were selected for during ancestral times.     

Phenotypic heterogeneity as key factor for growth and survival under oligotrophic conditions

Productivity-poor oligotrophic environments are plentiful on earth. Yet it is not well understood how organisms maintain population sizes under these extreme conditions. Most scenarios consider the adaptation of a single microorganism (isogenic) at the cellular level, which increases their fitness in such an environment. However, in oligotrophic environments, the adaptation of microorganisms at population level – that is, the ability of living cells to differentiate into subtypes with specialized attributes leading to the coexistence of different phenotypes in isogenic populations – remains a little-explored area of microbiology research. In this study, we performed experiments to demonstrate that an isogenic population differentiated to two subpopulations under low energy-flux in chemostats. Fluorescence cytometry and turnover rates revealed that these subpopulations differ in their nucleic acid content and metabolic activity. A mechanistic modelling framework for the dynamic adaptation of microorganisms with the consideration of their ability to switch between different phenotypes was experimentally calibrated and validated. Simulation of hypothetical scenarios suggests that responsive diversification upon a change in energy availability offers a competitive advantage over homogenous adaptation for maintaining viability and metabolic activity with time.     

The alexithymic brain: the neural pathways linking alexithymia to physical disorders

Alexithymia is a personality trait characterized by difficulties in identifying and describing feelings and is associated with psychiatric and psychosomatic disorders. The mechanisms underlying the link between emotional dysregulation and psychosomatic disorders are unclear. Recent progress in neuroimaging has provided important information regarding emotional experience in alexithymia. We have conducted three brain imaging studies on alexithymia, which we describe herein. This article considers the role of emotion in the development of physical symptoms and discusses a possible pathway that we have identified in our neuroimaging studies linking alexithymia with psychosomatic disorders. In terms of socio-affective processing, alexithymics demonstrate lower reactivity in brain regions associated with emotion. Many studies have reported reduced activation in limbic areas (e.g., cingulate cortex, anterior insula, amygdala) and the prefrontal cortex when alexithymics attempt to feel other people’s feelings or retrieve their own emotional episodes, compared to nonalexithymics. With respect to primitive emotional reactions such as the response to pain, alexithymics show amplified activity in areas considered to be involved in physical sensation. In addition to greater hormonal arousal responses in alexithymics during visceral pain, increased activity has been reported in the insula, anterior cingulate cortex, and midbrain. Moreover, in complex social situations, alexithymics may not be able to use feelings to guide their behavior appropriately. The Iowa gambling task (IGT) was developed to assess decision-making processes based on emotion-guided evaluation. When alexithymics perform the IGT, they fail to learn an advantageous decision-making strategy and show reduced activity in the medial prefrontal cortex, a key area for successful performance of the IGT, and increased activity in the caudate, a region associated with impulsive choice. The neural machinery in alexithymia is therefore activated more on the physiologic, motor-expressive level and less in the cognitive-experiential domains of the emotional response system. Affects may play an important role in alleviating intrinsic physiologic reactions and adapting to the environment. Deficient development of emotional neural structures may lead to hypersensitivity to bodily sensations and unhealthy behaviors, a possible mechanism linking alexithymia to psychosomatic disorders.     

Plant root growth, architecture and function

Without roots there would be no rhizosphere and no rhizodeposition to fuel microbial activity. Although micro-organisms may view roots merely as a source of carbon supply this belies the fascinating complexity and diversity of root systems that occurs despite their common function. Here, we examine the physiological and genetic determinants of root growth and the complex, yet varied and flexible, root architecture that results. The main functions of root systems are also explored including how roots cope with nutrient acquisition from the heterogeneous soil environment and their ability to form mutualistic associations with key soil micro-organisms (such as nitrogen fixing bacteria and mycorrhizal fungi) to aid them in their quest for nutrients. Finally, some key biotic and abiotic constraints on root development and function in the soil environment are examined and some of the adaptations roots have evolved to counter such stresses discussed.