Insect Mushroom Bodies Research Articles

The primacy model and the structure of olfactory space.

Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit.

The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body.

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies.

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body.

The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.

What do the mushroom bodies do for the insect brain? Twenty-five years of progress.

In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.

Minimal circuit motifs for second-order conditioning in the insect mushroom body.

In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body's intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

Decision-making: A new role for insect mushroom bodies

Mushroom bodies are neural structures that are ubiquitous in insect brains, where they integrate sensory inputs to encode stimulus identity and meaning. A new study now adds action selection- decision-making- to those roles.

Patterns of host plant use do not explain mushroom body expansion in Heliconiini butterflies.

The selective pressures leading to the elaboration of downstream, integrative processing centres, such as the mammalian neocortex or insect mushroom bodies, are often unclear. In Heliconius butterflies, the mushroom bodies are two to four times larger than those of their Heliconiini relatives, and the largest known in Lepidoptera. Heliconiini lay almost exclusively on Passiflora, which exhibit a remarkable diversity of leaf shape, and it has been suggested that the mushroom body expansion of Heliconius may have been driven by the cognitive demands of recognizing and learning leaf shapes of local host plants. We test this hypothesis using two complementary methods: (i) phylogenetic comparative analyses to test whether variation in mushroom body size is associated with the morphological diversity of host plants exploited across the Heliconiini; and (ii) shape-learning experiments using six Heliconiini species. We found that variation in the range of leaf morphologies used by Heliconiini was not associated with mushroom body volume. Similarly, we find interspecific differences in shape-learning ability, but Heliconius are not overall better shape learners than other Heliconiini. Together these results suggest that the visual recognition and learning of host plants was not a main factor driving the diversity of mushroom body size in this tribe.

Mushroom body output neurons MBON-a1/a2 define an odor intensity channel that regulates behavioral odor discrimination learning in larval Drosophila.

The sensitivity of animals to sensory input must be regulated to ensure that signals are detected and also discriminable. However, how circuits regulate the dynamic range of sensitivity to sensory stimuli is not well understood. A given odor is represented in the insect mushroom bodies (MBs) by sparse combinatorial coding by Kenyon cells (KCs), forming an odor quality representation. To address how intensity of sensory stimuli is processed at the level of the MB input region, the calyx, we characterized a set of novel mushroom body output neurons that respond preferentially to high odor concentrations. We show that a pair of MB calyx output neurons, MBON-a1/2, are postsynaptic in the MB calyx, where they receive extensive synaptic inputs from KC dendrites, the inhibitory feedback neuron APL, and octopaminergic sVUM1 neurons, but relatively few inputs from projection neurons. This pattern is broadly consistent in the third-instar larva as well as in the first instar connectome. MBON-a1/a2 presynaptic terminals innervate a region immediately surrounding the MB medial lobe output region in the ipsilateral and contralateral brain hemispheres. By monitoring calcium activity using jRCamP1b, we find that MBON-a1/a2 responses are odor-concentration dependent, responding only to ethyl acetate (EA) concentrations higher than a 200-fold dilution, in contrast to MB neurons which are more concentration-invariant and respond to EA dilutions as low as 10-4. Optogenetic activation of the calyx-innervating sVUM1 modulatory neurons originating in the SEZ (Subesophageal zone), did not show a detectable effect on MBON-a1/a2 odor responses. Optogenetic activation of MBON-a1/a2 using CsChrimson impaired odor discrimination learning compared to controls. We propose that MBON-a1/a2 form an output channel of the calyx, summing convergent sensory and modulatory input, firing preferentially to high odor concentration, and might affect the activity of downstream MB targets.

A neural theory for counting memories

Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. Here, we propose a theoretical two-layer neural circuit that stores counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements a count sketch using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories (“1-2-3-many”), which trades-off the number of categories that need to be distinguished with the potential ethological value of those categories. We show how both models can robustly track stimulus occurrence frequencies, thus expanding the traditional novelty-familiarity memory axis from binary to discrete with more than two possible values. Finally, we show that an implementation of the “1-2-3-many” count sketch exists in the insect mushroom body.

Mushroom body input connections form independently of sensory activity in Drosophila melanogaster

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons-namely those activated by a few ethologically meaningful odors-connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in partially anosmic flies-flies lacking the obligate odorant co-receptor Orco-and in wild-type flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.

Mushroom bodies and reniform bodies coexisting in crabs cannot both be homologs of the insect mushroom body.

In one species of shore crab (Brachyura, Varunidae), a center that supports long-term visual habituation and that matches the reniform body's morphology has been claimed as a homolog of the insect mushroom body despite lacking traits that define it as such. The discovery in a related species of shore crab of a mushroom body possessing those defining traits renders that interpretation unsound. Two phenotypically distinct, coexisting centers cannot both be homologs of the insect mushroom body. The present commentary outlines the history of research leading to misidentification of the reniform body as a mushroom body. One conclusion is that if both centers support learning and memory, this would be viewed as a novel and fascinating attribute of the pancrustacean brain.

Mushroom Bodies of Stick and Leaf Insects (Phasmatodea: Insecta): Structure and Sources of Formation

It has been established that mushroom bodies of stick insects are formed by Kenyon cells collected in numerous groups of cells the processes of which originally form individual bundles going into the calyx. The majority of such bundles lose their identity already in the cellular layer, and therefore, complex bundles usually enter the calyx. The merging of bundles progresses in the calyx, and even the peduncle keeps a fascicular structure. Only lobes have a classical concentric structure. In the embryonic period, mushroom bodies are formed by two proliferative centers with one neuroblast per center. It is supposed that the neuroblasts of mushroom bodies of stick insects are neuroblasts of type II and that groups of Kenyon cells are generated by intermediate neural progenitors.

Mushroom Body Input Connections Form Independently of Sensory Activity in <i>Drosophila melanogaster</i>

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons — namely those activated by a few ethologically meaningful odors — connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in anosmic flies — flies lacking the obligate odorant co-receptor Orco — and in wildtype flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.

A spiking neural program for sensorimotor control during foraging in flying insects

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

Correction: Nitric oxide acts as a cotransmitter in a subset of dopaminergic neurons to diversify memory dynamics

Animals employ diverse learning rules and synaptic plasticity dynamics to record temporal and statistical information about the world. However, the molecular mechanisms underlying this diversity are poorly understood. The anatomically defined compartments of the insect mushroom body function as parallel units of associative learning, with different learning rates, memory decay dynamics and flexibility (Aso and Rubin, 2016). Here, we show that nitric oxide (NO) acts as a neurotransmitter in a subset of dopaminergic neurons in Drosophila. NO's effects develop more slowly than those of dopamine and depend on soluble guanylate cyclase in postsynaptic Kenyon cells. NO acts antagonistically to dopamine; it shortens memory retention and facilitates the rapid updating of memories. The interplay of NO and dopamine enables memories stored in local domains along Kenyon cell axons to be specialized for predicting the value of odors based only on recent events. Our results provide key mechanistic insights into how diverse memory dynamics are established in parallel memory systems.

Tens versus Four: Two Generations of Neural Progenitors in the Developing Mushroom Bodies of Muscina prolapsa Harris (Diptera, Muscidae)

Neurogenesis in the mushroom bodies was studied throughout the larval and pupal development of Muscina prolapsa. The existence of two generations of neural progenitors in the mushroom bodies of insects was established for the first time. Each larval mushroom body has four neuroblasts that divide as type I neuroblasts, producing a daughter neuroblast and a ganglion mother cell. The latter divides symmetrically into two cells that differentiate into Kenyon cells. In the young pupae, 8–11 cells surrounding each neuroblast of the mushroom body and morphologically indistinguishable from ganglion mother cells grow in size and transform into secondary neuroblasts, which repeatedly divide asymmetrically into a daughter secondary neuroblast and a typical ganglion mother cell. Divisions of secondary neuroblasts continue from the 3rd to the 7th day after puparium formation, and during the 7th day, secondary neuroblasts die massively through apoptosis. The fate of four neuroblasts of the larval mushroom body remains obscure.

A crabs' high‐order brain center resolved as a mushroom body‐like structure

The hypothesis of a common origin for high-order memory centers in bilateral animals presents the question of how different brain structures, such as the vertebrate hippocampus and the arthropod mushroom bodies, are both structurally and functionally comparable. Obtaining evidence to support the hypothesis that crustaceans possess structures equivalent to the mushroom bodies that play a role in associative memories has proved challenging. Structural evidence supports that the hemiellipsoid bodies of hermit crabs, crayfish and lobsters, spiny lobsters, and shrimps are homologous to insect mushroom bodies. Although a preliminary description and functional evidence supporting such homology in true crabs (Brachyura) has recently been shown, other authors consider the identification of a possible mushroom body homolog in Brachyura as problematic. Here we present morphological and immunohistochemical data in Neohelice granulata supporting that crabs possess well-developed hemiellipsoid bodies that are resolved as mushroom bodies-like structures. Neohelice exhibits a peduncle-like tract, from which processes project into proximal and distal domains with different neuronal specializations. The proximal domains exhibit spines and en passant-like processes and are proposed here as regions mainly receiving inputs. The distal domains exhibit a "trauben"-like compartmentalized structure with bulky terminal specializations and are proposed here as output regions. In addition, we found microglomeruli-like complexes, adult neurogenesis, aminergic innervation, and elevated expression of proteins necessary for memory processes. Finally, in vivo calcium imaging suggests that, as in insect mushroom bodies, the output regions exhibit stimulus-specific activity. Our results support the shared organization of memory centers across crustaceans and insects.

Nomen est omen, cognitive dissonance, and homology of memory centers in crustaceans and insects.

In 1882, the Italian embryologist Giuseppe Bellonci introduced a nomenclature for structures in the stomatopod crustacean Squilla mantis that he claimed correspond to insect mushroom bodies, today recognized as cardinal centers that in insects mediate associative memory. The use of Bellonci's terminology has, through a series of misunderstandings and entrenched opinions, led to contesting views regarding whether centers in crustacean and insect brains that occupy corresponding locations and receive comparable multisensory inputs are homologous or homoplasic. The following describes the fate of terms used to denote sensory association neuropils in crustacean species and relates how those terms were deployed in the 1920s and 1930s by the Swedish neuroanatomist Bertil Hanström to claim homology in insects and crustaceans. Yet the same terminology has been repurposed by subsequent researchers to promote the very opposite view: that mushroom bodies are a derived trait of hexapods and that equivalent centers in crustaceans evolved independently.