Natural odors are often complex mixtures of different compounds. These mixtures can be perceived to have qualities that are different from their components. Moreover, components can be difficult to distinguish within a blend, even if those components are identifiable when presented individually. Thus, odor components can interact along the olfactory pathway in a nonlinear fashion such that the mixture is not perceived simply as the sum of its components. Here we investigated odor-evoked changes in Ca2+ concentration to binary blends of plant-related substances in individually identified glomeruli in the moth Spodoptera littoralis. We used a wide range of blend ratios and a range of concentrations below the level at which glomerular responses become saturated. We found no statistically significant cases where the mixture response was greater than both component responses at the same total concentration (synergistic interactions) and no statistically significant cases where the mixture response was less than either component presented individually (suppressive interactions). Therefore, we conclude that, for the plant mixtures studied, information of their components is preserved in the neural representations encoded at the first stage of olfactory processing in this moth species.

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.

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A key problem faced during the interpretation of calcium imaging data in sensory pathways is identifying brain regions which are be functionally equivalent, i.e. that respond in a correlated fashion to a set of stimuli presented over many trails -such as glomeruli are thought to be in the olfactory system. Here we present a novel automated method for detecting these correlations in neighbouring brain regions which uses only the physiological response characteristics to registrate them across individuals. After applying linear transformations for perspective correction, we show using this method how glomeruli positioning and response tuning during early olfactory coding is largely conserved across individuals of the same species, but there remains some variability. Our algorithm allows us to study the generalised properties of neural coding in different sensory systems or other brain regions, independently from such variations.