Biophysical modeling of information processing in the Drosophila olfactory system

Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. Although extensive studies have helped neurobiologists to understand the basic molecular and cellular mechanisms underlying information processing as well as principles of neural circuits of learning in the Drosophila olfactory system, there are still many questions that are awaiting an answer by neuroscience research. Specially, linking the observed behaviors including associative learning to underlying molecular mechanisms and neural circuitry are some challenges of modern neuroscience. In this research, we have aimed to present some models which are based on available data of the Drosophila olfactory system to describe the role of physiological as well as structural parameters in information procressing in the Drosophila olfactory system. For this purpose, we have presented an information theoretic approach to measure the system efficiency of the Drosophila olfactory system. We have studied the role of some parameters of the this system and of stimuli intensity in the environment with respect to how they influence the transmission of olfactory information. We have designed an abstract model of the antennal lobe, the mushroom body and the feedback inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of the intrinsic mushroom body neurons (Kenyon cells) was calculated to quantify the efficiency of information transmission. With this method we studied, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of the Kenyon cells. On the other hand, we analyzed the influence of inhibition on mutual information between environment and the mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting our expectations, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when feedback inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of feedback inhibition in Drosophila information processing without which the system efficiency will be substantially reduced. Associative learning acts as an important information processing mechanism whichis known in the Drosophila olfactory system where a given odor is associated with another stimulus as reward or punishment. To model the process in first and second order conditioning paradigm, the ’insilico-fly’ is introduced. These flies are neural networks that have been constructed using the available knowledge about neuronalmodel is based on the integration of synaptic and non-synaptic neural communications. The non-synaptic neural communication includes retrograde signal that may change the synaptic weight at both candidate places for associative learning in the Drosophila brain : 1. the synapses between the Kenyon cells and the output neuron and 2. the synaptic weights between the Kenyon cells and the projection neurons). In this model, the increased synaptic weight in both learning places are necessary to produce second-order conditioning, but after training, removing the synaptic weight between the Kenyon cells and the projection neurons does not impair second-order conditioning because these synapses gets high weight after presenting the trained odor to the neural system. The insilico-fly takes advantage of this feature because it allows specific association between odors and punishment (different odors may share some projection neurons). The insilico-fly illustrates the importance of integrating of systems biology and computational neuroscience to investigate complex mechanisms of behavioral studies in both Drosophila olfactory system or higher animals.