scholarly journals How the insect central complex could coordinate multimodal navigation

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Xuelong Sun ◽  
Shigang Yue ◽  
Michael Mangan
Keyword(s):  
Computational Models ◽  
Sensory Information ◽  
Visual Navigation ◽  
Previous Model ◽  
Central Complex ◽  
Frames Of Reference ◽  
Environmental Disturbances ◽  
Guidance Strategies ◽  
And Task

The central complex of the insect midbrain is thought to coordinate insect guidance strategies. Computational models can account for specific behaviours but their applicability across sensory and task domains remains untested. Here we assess the capacity of our previous model (Sun et al., 2020) of visual navigation to generalise to olfactory navigation and its coordination with other guidance in flies and ants. We show that fundamental to this capacity is the use of a biologically-plausible neural copy-and-shift mechanism that ensures sensory information is presented in a format compatible with the insect steering circuit regardless of its source. Moreover, the same mechanism is shown to allow the transfer cues from unstable/egocentric to stable/geocentric frames of reference providing a first account of the mechanism by which foraging insects robustly recover from environmental disturbances. We propose that these circuits can be flexibly repurposed by different insect navigators to address their unique ecological needs.

scholarly journals How the insect central complex could coordinate multimodal navigation

2021 ◽  
Author(s):  
Xuelong Sun ◽  
Shigang Yue ◽  
Michael Mangan
Keyword(s):  
Computational Models ◽  
Sensory Information ◽  
Visual Navigation ◽  
Previous Model ◽  
Central Complex ◽  
Frames Of Reference ◽  
Environmental Disturbances ◽  
Guidance Strategies ◽  
And Task

The central complex of the insect midbrain is thought to coordinate insect guidance strategies. Computational models can account for specific behaviours but their applicability across sensory and task domains remains untested. Here we assess the capacity of our previous model explaining visual navigation to generalise to olfactory navigation and its coordination with other guidance in flies and ants. We show that fundamental to this capacity is the use of a biologically-realistic neural copy-and-shift mechanism that ensures sensory information is presented in a format compatible with the insect steering circuit regardless of its source. Moreover, the same mechanism is shown to transfer cues from unstable/egocentric to stable/geocentric frames of reference providing a first account of the mechanism by which foraging insects robustly recover from environmental disturbances. We propose that these circuits can be flexibly repurposed by different insect navigators to address their unique ecological needs.

scholarly journals A neuronal circuit for vector computation builds an allocentric traveling-direction signal in the Drosophila fan-shaped body

2020 ◽  
Author(s):  
Cheng Lyu ◽  
L.F. Abbott ◽  
Gaby Maimon
Keyword(s):  
Computational Models ◽  
Neuronal Population ◽  
The Body ◽  
Central Complex ◽  
Two Dimensional ◽  
Neuronal Circuit ◽  
Head Direction ◽  
Neural Signals ◽  
Shaped Body ◽  
External Cues

AbstractMany behavioral tasks require the manipulation of mathematical vectors, but, outside of computational models1–8, it is not known how brains perform vector operations. Here we show how the Drosophila central complex, a region implicated in goal-directed navigation8–14, performs vector arithmetic. First, we describe neural signals in the fan-shaped body that explicitly track a fly’s allocentric traveling direction, that is, the traveling direction in reference to external cues. Past work has identified neurons in Drosophila12,15–17 and mammals18,19 that track allocentric heading (e.g., head-direction cells), but these new signals illuminate how the sense of space is properly updated when traveling and heading angles differ. We then characterize a neuronal circuit that rotates, scales, and adds four vectors related to the fly’s egocentric traveling direction–– the traveling angle referenced to the body axis––to compute the allocentric traveling direction. Each two-dimensional vector is explicitly represented by a sinusoidal activity pattern across a distinct neuronal population, with the sinusoid’s amplitude representing the vector’s length and its phase representing the vector’s angle. The principles of this circuit, which performs an egocentric-to-allocentric coordinate transformation, may generalize to other brains and to domains beyond navigation where vector operations or reference-frame transformations are required.

Control of Locomotion in Hexapods

2017 ◽  
pp. 422-438 ◽  
Author(s):  
Roy E. Ritzmann ◽  
Sasha N. Zill
Keyword(s):  
Control Systems ◽  
Local Control ◽  
Sensory Information ◽  
Legged Locomotion ◽  
Pattern Generation ◽  
Central Complex ◽  
Joint Movement ◽  
Control Circuits ◽  
Pattern Generators ◽  
The Brain

This article discusses legged locomotion in insects. It describes the basic patterns of coordinated movement both within each leg and among the various legs. The nervous system controls these actions through groups of joint pattern generators coupled through interneurons and interjoint reflexes in a range of insect species. These local control systems within the thoracic ganglia rely on leg proprioceptors that monitor joint movement and cuticular strain interacting with central pattern generation interneurons. The local control systems can change quantitatively and qualitatively as needed to generate turns or more forceful movements. In dealing with substantial obstacles or changes in navigational movements, more profound changes are required. These rely on sensory information processed in the brain that projects to the multimodal sensorimotor neuropils collectively referred to as the central complex. The central complex affects descending commands that alter local control circuits to accomplish appropriate redirected movements.

scholarly journals A lineage-related reciprocal inhibition circuitry for sensory-motor action selection

2017 ◽  
Author(s):  
Benjamin Kottler ◽  
Vincenzo G. Fiore ◽  
Zoe N. Ludlow ◽  
Edgar Buhl ◽  
Gerald Vinatier ◽  
...  
Keyword(s):  
Competitive Inhibition ◽  
Sensory Information ◽  
Action Selection ◽  
Reciprocal Inhibition ◽  
Central Complex ◽  
Genetic Manipulations ◽  
Behavioural Activity ◽  
Gating Mechanism ◽  
Sensory Motor ◽  
Motor Actions

ABSTRACTThe insect central complex and vertebrate basal ganglia are forebrain centres involved in selection and maintenance of behavioural actions. However, little is known about the formation of the underlying circuits, or how they integrate sensory information for motor actions. Here, we show that paired embryonic neuroblasts generate central complex ring neurons that mediate sensory-motor transformation and action selection in Drosophila. Lineage analysis resolves four ring neuron subtypes, R1-R4, that form GABAergic inhibition circuitry among inhibitory sister cells. Genetic manipulations, together with functional imaging, demonstrate subtype-specific R neurons mediate the selection and maintenance of behavioural activity. A computational model substantiates genetic and behavioural observations suggesting that R neuron circuitry functions as salience detector using competitive inhibition to amplify, maintain or switch between activity states. The resultant gating mechanism translates facilitation, inhibition and disinhibition of behavioural activity as R neuron functions into selection of motor actions and their organisation into action sequences.

scholarly journals Deficits in Prediction Ability Trigger Asymmetries in Behavior and Internal Representation

2020 ◽  
Vol 11 ◽  
Author(s):  
Anja Philippsen ◽  
Yukie Nagai
Keyword(s):  
Developmental Disorders ◽  
Computational Models ◽  
Predictive Coding ◽  
Sensory Information ◽  
Internal Representation ◽  
Human Perception ◽  
Underlying Mechanism ◽  
Autism Spectrum ◽  
Prediction Ability ◽  
And Behavior

Predictive coding is an emerging theoretical framework for explaining human perception and behavior. The proposed underlying mechanism is that signals encoding sensory information are integrated with signals representing the brain's prior prediction. Imbalance or aberrant precision of the two signals has been suggested as a potential cause for developmental disorders. Computational models may help to understand how such aberrant tendencies in prediction affect development and behavior. In this study, we used a computational approach to test the hypothesis that parametric modifications of prediction ability generate a spectrum of network representations that might reflect the spectrum from typical development to potential disorders. Specifically, we trained recurrent neural networks to draw simple figure trajectories, and found that altering reliance on sensory and prior signals during learning affected the networks' performance and the emergent internal representation. Specifically, both overly strong or weak reliance on predictions impaired network representations, but drawing performance did not always reflect this impairment. Thus, aberrant predictive coding causes asymmetries in behavioral output and internal representations. We discuss the findings in the context of autism spectrum disorder, where we hypothesize that too weak or too strong a reliance on predictions may be the cause of the large diversity of symptoms associated with this disorder.

scholarly journals External drivers of BOLD signal’s non-stationarity

2021 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Computational Models ◽  
Linear Time ◽  
Brain Activity ◽  
External Input ◽  
Imaging Data ◽  
External Stimulation ◽  
Cortical Input ◽  
Linear Time Invariant ◽  
Human Connectome Project ◽  
And Task

AbstractA fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. Together, we provide evidence that this embodied brain activity model offers information about the structure and dimensionality of the BOLD signal’s external drivers and shines light on likely external sources contributing to the BOLD signal’s non-stationarity.

scholarly journals Computational Mechanism for the Effect of Psychosis Community Treatment: A Conceptual Review From Neurobiology to Social Interaction

2021 ◽  
Vol 12 ◽  
Author(s):  
David Benrimoh ◽  
Andrew Sheldon ◽  
Ely Sibarium ◽  
Albert R. Powers
Keyword(s):  
Computational Models ◽  
Sensory Information ◽  
Community Treatment ◽  
Psychotic Symptoms ◽  
Cognitive Resources ◽  
The Social ◽  
Multiple Levels ◽  
New Treatments ◽  
Therapeutic Research ◽  
Putative Mechanism

The computational underpinnings of positive psychotic symptoms have recently received significant attention. Candidate mechanisms include some combination of maladaptive priors and reduced updating of these priors during perception. A potential benefit of models with such mechanisms is their ability to link multiple levels of explanation, from the neurobiological to the social, allowing us to provide an information processing-based account of how specific alterations in self-self and self-environment interactions result in the experience of positive symptoms. This is key to improving how we understand the experience of psychosis. Moreover, it points us toward more comprehensive avenues for therapeutic research by providing a putative mechanism that could allow for the generation of new treatments from first principles. In order to demonstrate this, our conceptual paper will discuss the application of the insights from previous computational models to an important and complex set of evidence-based clinical interventions with strong social elements, such as coordinated specialty care clinics (CSC) in early psychosis and assertive community treatment (ACT). These interventions may include but also go beyond psychopharmacology, providing, we argue, structure and predictability for patients experiencing psychosis. We develop the argument that this structure and predictability directly counteract the relatively low precision afforded to sensory information in psychosis, while also providing the patient more access to external cognitive resources in the form of providers and the structure of the programs themselves. We discuss how computational models explain the resulting reduction in symptoms, as well as the predictions these models make about potential responses of patients to modifications or to different variations of these interventions. We also link, via the framework of computational models, the patient's experiences and response to interventions to putative neurobiology.

Insect Navigation: Neural Basis to Behavior

2018 ◽  
Author(s):  
Stanley Heinze
Keyword(s):  
Path Integration ◽  
Sensory Information ◽  
Central Complex ◽  
Insect Brain ◽  
Head Direction ◽  
Neural Basis ◽  
Long Distance ◽  
Insect Navigation ◽  
Straight Line ◽  
Wide Range

Navigation is the ability of animals to move through their environment in a planned manner. Different from directed but reflex-driven movements, it involves the comparison of the animal’s current heading with its intended heading (i.e., the goal direction). When the two angles don’t match, a compensatory steering movement must be initiated. This basic scenario can be described as an elementary navigational decision. Many elementary decisions chained together in specific ways form a coherent navigational strategy. With respect to navigational goals, there are four main forms of navigation: explorative navigation (exploring the environment for food, mates, shelter, etc.); homing (returning to a nest); straight-line orientation (getting away from a central place in a straight line); and long-distance migration (seasonal long-range movements to a location such as an overwintering place). The homing behavior of ants and bees has been examined in the most detail. These insects use several strategies to return to their nest after foraging, including path integration, route following, and, potentially, even exploit internal maps. Independent of the strategy used, insects can use global sensory information (e.g., skylight cues), local cues (e.g., visual panorama), and idiothetic (i.e., internal, self-generated) cues to obtain information about their current and intended headings. How are these processes controlled by the insect brain? While many unanswered questions remain, much progress has been made in recent years in understanding the neural basis of insect navigation. Neural pathways encoding polarized light information (a global navigational cue) target a brain region called the central complex, which is also involved in movement control and steering. Being thus placed at the interface of sensory information processing and motor control, this region has received much attention recently and emerged as the navigational “heart” of the insect brain. It houses an ordered array of head-direction cells that use a wide range of sensory information to encode the current heading of the animal. At the same time, it receives information about the movement speed of the animal and thus is suited to compute the home vector for path integration. With the help of neurons following highly stereotypical projection patterns, the central complex theoretically can perform the comparison of current and intended heading that underlies most navigation processes. Examining the detailed neural circuits responsible for head-direction coding, intended heading representation, and steering initiation in this brain area will likely lead to a solid understanding of the neural basis of insect navigation in the years to come.

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