Separate But Interactive Parallel Olfactory Processing Streams Governed by Different Types of GABAergic Feedback Neurons in the Mushroom Body of a Basal Insect

Parallel olfactory processing in a hemimetabolous insect

To represent specific olfactory cues from the highly complex and dynamic odor world in the brain, insects employ multiple parallel olfactory pathways that process odors with different coding strategies. Here, we summarize the anatomical and physiological features of parallel olfactory pathways in the hemimetabolous insect, the cockroach Periplaneta americana. The cockroach processes different aspects of odor stimuli, such as odor qualities, temporal information, and dynamics, through parallel olfactory pathways. These parallel pathways are anatomically segregated from the peripheral to higher brain centers, forming functional maps within the brain. In addition, the cockroach may possess parallel pathways that correspond to distinct types of olfactory receptors expressed in sensory neurons. Through comparisons with olfactory pathways in holometabolous insects, we aim to provide valuable insights into the organization, functionality, and evolution of insect olfaction.

Roles of octopamine neurons in the vertical lobe of the mushroom body for the execution of a conditioned response in cockroaches

Aminergic neurons mediate reward signals in mammals and insects. In crickets, we showed that blockade of synaptic transmission from octopamine neurons (OANs) impairs conditioning of an odor (conditioned stimulus, CS) with water or sucrose (unconditioned stimulus, US) and execution of a conditioned response (CR) to the CS. It has not yet been established, however, whether findings in crickets can be applied to other species of insects. In this study, we investigated the roles of OANs in conditioning of salivation, monitored by activities of salivary neurons, and in execution of the CR in cockroaches (Periplaneta americana). We showed that injection of epinastine (an OA receptor antagonist) into the head hemolymph impaired both conditioning and execution of the CR, in accordance with findings in crickets. Moreover, local injection of epinastine into the vertical lobes of the mushroom body (MB), the center for associative learning and control of the CR, impaired execution of the CR, whereas injection of epinastine into the calyces of the MB or the antennal lobes (primary olfactory centers) did not. We propose that OANs in the MB vertical lobes play critical roles in the execution of the CR in cockroaches. This is analogous to the fact that midbrain dopamine neurons govern execution of learned actions in mammals.

Learning and memory in the orange head cockroach (Eublaberus posticus)

This paper describes two experiments aimed at establishing the orange head cockroach (Eublaberus posticus) as a model organism for behavioral research. While many invertebrate models are available, cockroaches have several benefits over others that show impressive behavioral abilities. Most notably, cockroaches are long-lived generalists that can be maintained in controlled indoor laboratory conditions. While the most popular cockroaches in behavioral research, Periplaneta americana and Blattella germanica, have the potential to become domestic pests, our E. posticus is extremely unlikely to escape or infest a human environment, making it a very practical species. In our first experiment, we investigated the ability of E. posticus to associate novel odors with appetitive and aversive solutions. They quickly learned to approach odors associated with a dog food sucrose solution and learned to avoid odors associated with salt water. The second experiment repeated the methods of the first experiment, while also testing retained preferences for conditioned odors, from 15 to 1,215 minutes after the conditioning procedure ended. We found that preferences for odors associated with food were strongest 45 minutes after training, then decreased as a function of time. Our work is the first to show associative learning and memory in the orange head cockroach. Findings are discussed in comparison to other invertebrate models as well as to other cockroach research.

Silencing the odorant receptor co-receptor impairs olfactory reception in a sensillum-specific manner in the cockroach

Insects detect odors via a large variety of odorant receptors (ORs) expressed in olfactory sensory neurons (OSNs). The insect OR is a heteromeric complex composed of a ligand-specific receptor and the co-receptor (ORco). In this study, we identified the ORco gene of the cockroach, Periplaneta americana (PameORco), and performed RNAi-based functional analysis of PameORco. All OSNs in the basiconic sensilla expressed PameORco and received a large variety of odors including sex pheromones. In trichoid sensilla, a PameORco-positive OSN was consistently paired with a PameORco-negative OSN tuned to acids. In adult cockroaches injected with PameORco dsRNA at the nymphal stage, the expression of PameORco, odor receptions via ORs, and its central processing were strongly suppressed. These results provide new insights into the molecular basis of olfactory reception in the cockroach. The long-lasting and irreversible effects of PameORco RNAi would be an effective method for controlling the household pest.

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Whole sequence of ORco in the American cockroach (PameORco) was characterized

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PameORco expressed in olfactory sensory neurons in a sensillar type-specific manner

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RNAi chronically and irreversibly suppressed the PameORco expression beyond molts

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PameORco was essential for receptions of sex pheromones and general odors

The best of both worlds: Dual systems of reasoning in animals and AI

Much of human cognition involves two different types of reasoning that operate together. Type 1 reasoning systems are intuitive and fast, whereas Type 2 reasoning systems are reflective and slow. Why has our cognition evolved with these features? Both systems are coherent and in most ecological circumstances either alone is capable of coming up with the right answer most of the time. Neural tissue is costly, and thus far evolutionary models have struggled to identify a benefit of operating two systems of reasoning. To explore this issue we take a broad comparative perspective. We discuss how dual processes of cognition have enabled the emergence of selective attention in insects, transforming the learning capacities of these animals. Modern AIs using dual systems of learning are able to learn how their vast world works and how best to interact with it, allowing them to exceed human levels of performance in strategy games. We propose that the core benefits of dual processes of reasoning are to narrow down a problem space in order to focus cognitive resources most effectively.

Distribution and daily oscillation of GABA in the circadian system of the cockroach Rhyparobia maderae

Gamma-aminobutyric acid (GABA) is the prevalent inhibitory neurotransmitter in nervous systems promoting sleep in both mammals and insects. In the Madeira cockroach, sleep-wake cycles are controlled by a circadian clock network in the brain's optic lobes, centered in the accessory medulla (AME) with its innervating pigment-dispersing factor (PDF) expressing clock neurons at the anterior-ventral rim of the medulla. GABA is present in cell clusters that innervate different circuits of the cockroach's AME clock, without colocalizing in PDF clock neurons. Physiological, immunohistochemical, and behavioral assays provided evidence for a role of GABA in light entrainment, possibly via the distal tract that connects the AME's glomeruli to the medulla. Furthermore, GABA was implemented in clock outputs to multiple effector systems in optic lobe and midbrain. Here, GABAergic brain circuits were analyzed further, focusing on the circadian system in search for sleep/wake controlling brain circuits. All GABA-immunoreactive neurons of the cockroach brain were also stained with an antiserum against the GABA-synthesizing enzyme glutamic acid decarboxylase. We found strong overlap of the distribution of GABA-immunoreactive networks with PDF clock networks in optic lobes and midbrain. Neurons in five of the six soma groups that innervate the clock exhibited GABA immunoreactivity. The intensity of GABA immunoreactivity in the distal tract showed daily fluctuations with maximum staining intensity in the middle of the day and weakest staining at the end of the day. Quantification via enzyme-linked immunosorbent assay and quantitative liquid chromatography coupled to electrospray ionization tandem mass spectrometry, likewise, showed higher GABA levels in the optic lobe during the inactivity phase of the cockroach during the day and lower levels during its activity phase at dusk. Our data further support the hypothesis that light- and PDF-dependently the circadian clock network of the cockroach controls GABA levels and thereby promotes sleep during the day.

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.