Studying small brains to understand the building blocks of cognition

Hierarchical Modular Structure of the Drosophila Connectome

The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts.Significance Statement The Hemibrain is a partial connectome of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function.

Visual processing and collective motion-related decision-making in desert locusts

Collectively moving groups of animals rely on the decision-making of locally interacting individuals in order to maintain swarm cohesion. However, the complex and noisy visual environment poses a major challenge to the extraction and processing of relevant information. We addressed this challenge by studying swarming-related decision-making in desert locust last-instar nymphs. Controlled visual stimuli, in the form of random dot kinematograms, were presented to tethered locust nymphs in a trackball set-up, while monitoring movement trajectory and walking parameters. In a complementary set of experiments, the neurophysiological basis of the observed behavioural responses was explored. Our results suggest that locusts use filtering and discrimination upon encountering multiple stimuli simultaneously. Specifically, we show that locusts are sensitive to differences in speed at the individual conspecific level, and to movement coherence at the group level, and may use these to filter out non-relevant stimuli. The locusts also discriminate and assign different weights to different stimuli, with an observed interactive effect of stimulus size, relative abundance and motion direction. Our findings provide insights into the cognitive abilities of locusts in the domain of decision-making and visual-based collective motion, and support locusts as a model for investigating sensory-motor integration and motion-related decision-making in the intricate swarm environment.

Differential mechanisms underlie trace and delay conditioning in Drosophila

Two forms of associative learning-delay conditioning and trace conditioning-have been widely investigated in humans and higher-order mammals¹. In delay conditioning, an unconditioned stimulus (for example, an electric shock) is introduced in the final moments of a conditioned stimulus (for example, a tone), with both ending at the same time. In trace conditioning, a 'trace' interval separates the conditioned stimulus and the unconditioned stimulus. Trace conditioning therefore relies on maintaining a neural representation of the conditioned stimulus after its termination (hence making distraction possible²), to learn the conditioned stimulus-unconditioned stimulus contingency³; this makes it more cognitively demanding than delay conditioning⁴. Here, by combining virtual-reality behaviour with neurogenetic manipulations and in vivo two-photon brain imaging, we show that visual trace conditioning and delay conditioning in Drosophila mobilize R2 and R4m ring neurons in the ellipsoid body. In trace conditioning, calcium transients during the trace interval show increased oscillations and slower declines over repeated training, and both of these effects are sensitive to distractions. Dopaminergic activity accompanies signal persistence in ring neurons, and this is decreased by distractions solely during trace conditioning. Finally, dopamine D1-like and D2-like receptor signalling in ring neurons have different roles in delay and trace conditioning; dopamine D1-like receptor 1 mediates both forms of conditioning, whereas the dopamine D2-like receptor is involved exclusively in sustaining ring neuron activity during the trace interval of trace conditioning. These observations are similar to those previously reported in mammals during arousal⁵, prefrontal activation⁶ and high-level cognitive learning^7,8.

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

Two views on the cognitive brain

Cognition can be defined as computation over meaningful representations in the brain to produce adaptive behaviour. There are two views on the relationship between cognition and the brain that are largely implicit in the literature. The Sherringtonian view seeks to explain cognition as the result of operations on signals performed at nodes in a network and passed between them that are implemented by specific neurons and their connections in circuits in the brain. The contrasting Hopfieldian view explains cognition as the result of transformations between or movement within representational spaces that are implemented by neural populations. Thus, the Hopfieldian view relegates details regarding the identity of and connections between specific neurons to the status of secondary explainers. Only the Hopfieldian approach has the representational and computational resources needed to develop novel neurofunctional objects that can serve as primary explainers of cognition.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Oscillations in the central brain of Drosophila are phase locked to attended visual features

Object-based attention describes the brain's capacity to prioritize one set of stimuli while ignoring others. Human research suggests that the binding of diverse stimuli into one attended percept requires phase-locked oscillatory activity in the brain. Even insects display oscillatory brain activity during visual attention tasks, but it is unclear if neural oscillations in insects are selectively correlated to different features of attended objects. We addressed this question by recording local field potentials in the Drosophila central complex, a brain structure involved in visual navigation and decision making. We found that attention selectively increased the neural gain of visual features associated with attended objects and that attention could be redirected to unattended objects by activation of a reward circuit. Attention was associated with increased beta (20- to 30-Hz) oscillations that selectively locked onto temporal features of the attended visual objects. Our results suggest a conserved function for the beta frequency range in regulating selective attention to salient visual features.

The Fly Brain Atlas

The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains that can be examined and manipulated by genetic means offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In Drosophila, two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord.

Insect Neurobiology: What to Make of a Small Spot?

Vertical stripes have long been known to attract laboratory fruit flies, but small spots, presumably common in the wild, seemed only to avert them. A new study finds odor can reverse this, demonstrating that flies respond to shapes more judiciously than was thought.

Training bioinspired sensors to classify flows

We consider the inverse problem of classifying flow patterns from local sensory measurements. This problem is inspired by the ability of various aquatic organisms to respond to ambient flow signals, and is relevant for translating these abilities to underwater robotic vehicles. In Colvert, Alsalman and Kanso, B&B (2018), we trained neural networks to classify vortical flows by relying on a single flow sensor that measures a 'time history' of the local vorticity. Here, we systematically investigate the effects of distinct types of sensors on the accuracy of flow classification. We consider four types of sensors-vorticity, flow velocities parallel and transverse to the direction of flow propagation, and flow speed-and show that the networks trained using transverse velocity outperform other networks, even when subjected to aggressive data corruption. We then train the network to classify flow patterns instantaneously, using a spatially-distributed array of sensors and a single 'one time' sensory measurement. The network, based on a handful of spatially-distributed sensors, exhibits remarkable accuracy in flow classification. These results lay the groundwork for developing learning algorithms for the dynamic deployment of sensory arrays in unsteady flows.

Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics

To understand the brain, molecular details need to be overlaid onto neural wiring diagrams so that synaptic mode, neuromodulation and critical signaling operations can be considered. Single-cell transcriptomics provide a unique opportunity to collect this information. Here we present an initial analysis of thousands of individual cells from Drosophila midbrain, that were acquired using Drop-Seq. A number of approaches permitted the assignment of transcriptional profiles to several major brain regions and cell-types. Expression of biosynthetic enzymes and reuptake mechanisms allows all the neurons to be typed according to the neurotransmitter or neuromodulator that they produce and presumably release. Some neuropeptides are preferentially co-expressed in neurons using a particular fast-acting transmitter, or monoamine. Neuromodulatory and neurotransmitter receptor subunit expression illustrates the potential of these molecules in generating complexity in neural circuit function. This cell atlas dataset provides an important resource to link molecular operations to brain regions and complex neural processes.

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

Connecting the Brain to Itself through an Emulation

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control—enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Visual motion processing subserving behavior in crabs

Motion vision originated during the Cambrian explosion more than 500 million years ago, likely triggered by the race for earliest detection between preys and predators. To successfully evade a predator's attack a prey must react quickly and reliably, which imposes a common constrain to the implementation of escape responses among different species. Thus, neural circuits subserving fast escape responses are usually straightforward and contain giant neurons. This review summarizes knowledge about a small group of motion-sensitive giant neurons thought to be central in guiding the escape performance of crabs to visual stimuli. The flexibility of the escape behavior contrasts with the stiffness of the optomotor response, indicating a task-dependent early segregation of visual pathways.

On the Encoding of Panoramic Visual Scenes in Navigating Wood Ants

A natural visual panorama is a complex stimulus formed of many component shapes. It gives an animal a sense of place and supplies guiding signals for controlling the animal's direction of travel [1]. Insects with their economical neural processing [2] are good subjects for analyzing the encoding and memory of such scenes [3-5]. Honeybees [6] and ants [7, 8] foraging from their nest can follow habitual routes guided only by visual cues within a natural panorama. Here, we analyze the headings that ants adopt when a familiar panorama composed of two or three shapes is manipulated by removing a shape or by replacing training shapes with unfamiliar ones. We show that (1) ants recognize a component shape not only through its particular visual features, but also by its spatial relation to other shapes in the scene, and that (2) each segmented shape [9] contributes its own directional signal to generating the ant's chosen heading. We found earlier that ants trained to a feeder placed to one side of a single shape [10] and tested with shapes of different widths learn the retinal position of the training shape's center of mass (CoM) [11, 12] when heading toward the feeder. They then guide themselves by placing the shape's CoM in the remembered retinal position [10]. This use of CoM in a one-shape panorama combined with the results here suggests that the ants' memory of a multi-shape panorama comprises the retinal positions of the horizontal CoMs of each major component shape within the scene, bolstered by local descriptors of that shape.

Learning and Memory in Disease Vector Insects

Learning and memory plays an important role in host preference and parasite transmission by disease vector insects. Historically there has been a dearth of standardized protocols that permit testing their learning abilities, thus limiting discussion on the potential epidemiological consequences of learning and memory to a largely speculative extent. However, with increasing evidence that individual experience and associative learning can affect processes such as oviposition site selection and host preference, it is timely to review the recently acquired knowledge, identify research gaps and discuss the implication of learning in disease vector insects in perspective with control strategies.

Evidence for selective attention in the insect brain

The capacity for selective attention appears to be required by any animal responding to an environment containing multiple objects, although this has been difficult to study in smaller animals such as insects. Clear operational characteristics of attention however make study of this crucial brain function accessible to any animal model. Whereas earlier approaches have relied on freely behaving paradigms placed in an ecologically relevant context, recent tethered preparations have focused on brain imaging and electrophysiology in virtual reality environments. Insight into brain activity during attention-like behavior has revealed key elements of attention in the insect brain. Surprisingly, a variety of brain structures appear to be involved, suggesting that even in the smallest brains attention might involve widespread coordination of neural activity.