A model study on the circuit mechanism underlying decision-making in Drosophila

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

A neural algorithm for Drosophila linear and nonlinear decision-making

It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. First, our SNN model could successfully describe all the experimental findings in fly visual reinforcement learning and action selection among multiple conflicting choices as well. Second, our computational modeling shows that dopaminergic neuron-GABAergic neuron-mushroom body (DA-GABA-MB) works in a recurrent loop providing a key circuit for gain and gating mechanism of nonlinear decision making. Compared with existing models, our model shows more biologically plausible on the network design and working mechanism, and could amplify the small differences between two conflicting cues more clearly. Finally, based on the proposed model, the UAV could quickly learn to make clear-cut decisions among multiple visual choices and flexible reversal learning resembling to real fly. Compared with linear and uniform decision-making methods, the DA-GABA-MB mechanism helps UAV complete the decision-making task with fewer steps.

A neural model of the frontal eye fields with reward-based learning

Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, using computational simulations to explain the cognitive process of choice tasks. The core of this extended model has three aspects: direction-preferred populations that cluster together the neurons with the same orientation preference, rule modules that control different rule-dependent activities, and reward-based synaptic plasticity that modulates connections to flexibly change the decision according to task demands. After repeated attempts in a number of trials, the network successfully simulated three decision choice tasks: an anti-saccade task, a no-go task, and an associative task. We found that synaptic plasticity could modulate the competition of choices by suppressing erroneous choices while enhancing the correct (rewarding) choice. In addition, the trained model captured some properties exhibited in animal and human experiments, such as the latency of the reaction time distribution of anti-saccades, the stop signal mechanism for canceling a reflexive saccade, and the variation of latency to half-max selectivity. Furthermore, the trained model was capable of reproducing the re-learning procedures when switching tasks and reversing the cue-saccade association.

Computational models to understand decision making and pattern recognition in the insect brain

Odor stimuli reaching olfactory systems of mammals and insects are characterized by remarkable non-stationary and noisy time series. Their brains have evolved to discriminate subtle changes in odor mixtures and find meaningful variations in complex spatio-temporal patterns. Insects with small brains can effectively solve two computational tasks: identify the presence of an odor type and estimate the concentration levels of the odor. Understanding the learning and decision making processes in the insect brain can not only help us to uncover general principles of information processing in the brain, but it can also provide key insights to artificial chemical sensing. Both olfactory learning and memory are dominantly organized in the Antennal Lobe (AL) and the Mushroom Bodies (MBs). Current computational models yet fail to deliver an integrated picture of the joint computational roles of the AL and MBs. This review intends to provide an integrative overview of the computational literature analyzed in the context of the problem of classification (odor discrimination) and regression (odor concentration estimation), particularly identifying key computational ingredients necessary to solve pattern recognition.

Presynaptic inhibition of gamma lobe neurons is required for olfactory learning in Drosophila

The loss of heterotrimeric G(o) signaling through the expression of pertussis toxin (PTX) within either the α/β or γ lobe mushroom body neurons of Drosophila results in the impaired aversive olfactory associative memory formation. Herein, we focus on the cellular effects of G(o) signaling in the γ lobe mushroom body neurons during memory formation. Expression of PTX in the γ lobes specifically inhibits G(o) activation, leading to poor olfactory learning and an increase in odor-elicited synaptic vesicle release. In the γ lobe neurons, training decreases synaptic vesicle release elicited by the unpaired conditioned stimulus -, while leaving presynaptic activation by the paired conditioned stimulus + unchanged. PTX expression in γ lobe neurons inhibits the generation of this differential synaptic activation by conditioned stimuli after negative reinforcement. Hyperpolarization of the γ lobe neurons or the inhibition of presynaptic activity through the expression of dominant negative dynamin transgenes ameliorated the memory impairment caused by PTX, indicating that the disinhibition of these neurons by PTX was responsible for the poor memory formation. The role for γ lobe inhibition, carried out by G(o) activation, indicates that an inhibitory circuit involving these neurons plays a positive role in memory acquisition. This newly uncovered requirement for inhibition of odor-elicited activity within the γ lobes is consistent with these neurons serving as comparators during learning, perhaps as part of an odor salience modification mechanism.

Grand Research Plan for Neural Circuits of Emotion and Memory--current status of neural circuit studies in China

During recent years, major advances have been made in neuroscience, i.e., asynchronous release, three-dimensional structural data sets, saliency maps, magnesium in brain research, and new functional roles of long non-coding RNAs. Especially, the development of optogenetic technology provides access to important information about relevant neural circuits by allowing the activation of specific neurons in awake mammals and directly observing the resulting behavior. The Grand Research Plan for Neural Circuits of Emotion and Memory was launched by the National Natural Science Foundation of China. It takes emotion and memory as its main objects, making the best use of cutting-edge technologies from medical science, life science and information science. In this paper, we outline the current status of neural circuit studies in China and the technologies and methodologies being applied, as well as studies related to the impairments of emotion and memory. In this phase, we are making efforts to repair the current deficiencies by making adjustments, mainly involving four aspects of core scientific issues to investigate these circuits at multiple levels. Five research directions have been taken to solve important scientific problems while the Grand Research Plan is implemented. Future research into this area will be multimodal, incorporating a range of methods and sciences into each project. Addressing these issues will ensure a bright future, major discoveries, and a higher level of treatment for all affected by debilitating brain illnesses.

Tissue-specific activation of a single gustatory receptor produces opposing behavioral responses in Drosophila

Understanding sensory systems that perceive environmental inputs and neural circuits that select appropriate motor outputs is essential for studying how organisms modulate behavior and make decisions necessary for survival. Drosophila melanogaster oviposition is one such important behavior, in which females evaluate their environment and choose to lay eggs on substrates they may find aversive in other contexts. We employed neurogenetic techniques to characterize neurons that influence the choice between repulsive positional and attractive egg-laying responses toward the bitter-tasting compound lobeline. Surprisingly, we found that neurons expressing Gr66a, a gustatory receptor normally involved in avoidance behaviors, receive input for both attractive and aversive preferences. We hypothesized that these opposing responses may result from activation of distinct Gr66a-expressing neurons. Using tissue-specific rescue experiments, we found that Gr66a-expressing neurons on the legs mediate positional aversion. In contrast, pharyngeal taste cells mediate the egg-laying attraction to lobeline, as determined by analysis of mosaic flies in which subsets of Gr66a neurons were silenced. Finally, inactivating mushroom body neurons disrupted both aversive and attractive responses, suggesting that this brain structure is a candidate integration center for decision-making during Drosophila oviposition. We thus define sensory and central neurons critical to the process by which flies decide where to lay an egg. Furthermore, our findings provide insights into the complex nature of gustatory perception in Drosophila. We show that tissue-specific activation of bitter-sensing Gr66a neurons provides one mechanism by which the gustatory system differentially encodes aversive and attractive responses, allowing the female fly to modulate her behavior in a context-dependent manner.