Learning Set Formation and Reversal Learning in Mice During High-Throughput Home-Cage-Based Olfactory Discrimination

Rodent behavioral tasks are crucial to understanding the nature and underlying biology of cognition and cognitive deficits observed in psychiatric and neurological pathologies. Olfaction, as the primary sensory modality in rodents, is widely used to investigate cognition in rodents. In recent years, automation of olfactory tasks has made it possible to conduct olfactory experiments in a time- and labor-efficient manner while also minimizing experimenter-induced variability. In this study, we bring automation to the next level in two ways: First, by incorporating a radio frequency identification-based sorter that automatically isolates individuals for the experimental session. Thus, we can not only test animals during defined experimental sessions throughout the day but also prevent cagemate interference during task performance. Second, by implementing software that advances individuals to the next test stage as soon as performance criteria are reached. Thus, we can prevent overtraining, a known confounder especially in cognitive flexibility tasks. With this system in hand, we trained mice on a series of four odor pair discrimination tasks as well as their respective reversals. Due to performance-based advancement, mice normally advanced to the next stage in less than a day. Over the series of subsequent odor pair discriminations, the number of errors to criterion decreased significantly, thus indicating the formation of a learning set. As expected, errors to criterion were higher during reversals. Our results confirm that the system allows investigating higher-order cognitive functions such as learning set formation (which is understudied in mice) and reversal learning (which is a measure of cognitive flexibility and impaired in many clinical populations). Therefore, our system will facilitate investigations into the nature of cognition and cognitive deficits in pathological conditions by providing a high-throughput and labor-efficient experimental approach without the risks of overtraining or cagemate interference.

Dynamics of Input Patterns Modulate the Behavior of a Model of Olfactory Bulb Function

Input patterns to the olfactory bulb are dynamic and change in an odor-specific manner as measured by selective calcium imaging of olfactory bulb input. To our knowledge, none of the published models of olfactory bulb function uses dynamic input patterns. Therefore we tested how dynamic input alters the behavior of a simple model consisting of two layers. The membrane potential of the first-layer neurons, integrate-and-fire neurons corresponding to mitral cells, was modulated with a subthreshold oscillation at respiration frequency. The membrane potential of the second-layer neurons was used to discriminate input patterns. We implemented oscillating input with amplitudes and latencies different for each mitral cell. Not only varying the input amplitudes but also de-synchronizing the input, and varying the relation between latency and input amplitude, individually changed the model's performance significantly. The discrimination time was affected more easily than the number of second-layer neurons that can differentiate an odor pair. Increasing the de-synchronization, i.e., the spread of latency values, reduced the differences in response time between strong and weak stimulus pairs without reducing the number of reacting cells. Input phase relative to the subthreshold oscillation altered the effect of de-synchronization. Thus dynamic input changes performance parameters of models of olfactory information processing that can be verified experimentally.

Experience-Dependent Neural Integration of Taste and Smell in the Human Brain

Flavor perception arises from the central integration of peripherally distinct sensory inputs (taste, smell, texture, temperature, sight, and even sound of foods). The results from psychophysical and neuroimaging studies in humans are converging with electrophysiological findings in animals and a picture of the neural correlates of flavor processing is beginning to emerge. Here we used event-related fMRI to evaluate brain response during perception of flavors (i.e., taste/odor liquid mixtures not differing in temperature or texture) compared with the sum of the independent presentation of their constituents (taste and/or odor). All stimuli were presented in liquid form so that olfactory stimulation was by the retronasal route. Mode of olfactory delivery is important because neural suppression has been observed in chemosensory regions during congruent taste–odor pairs when the odors are delivered by the orthonasal route and require subjects to sniff. There were 2 flavors. One contained a familiar/congruent taste–odor pair (vanilla/sweet) and the other an unfamiliar/incongruent taste–odor pair (vanilla/salty). Three unimodal stimuli, including 2 tastes (sweet and salty) and one odor (vanilla), as well as a tasteless/odorless liquid (baseline) were presented. Superadditive responses during the perception of the congruent flavor compared with the sum of its constituents were observed in the anterior cingulate cortex (ACC), dorsal insula, anterior ventral insula extending into the caudal orbitofrontal cortex (OFC), frontal operculum, ventral lateral prefrontal cortex, and posterior parietal cortex. These regions were not present in a similar analysis of the incongruent flavor compared with the sum of its constituents. All of these regions except the ventrolateral prefrontal cortex were also isolated in a direct contrast of congruent − incongruent. Additionally, the anterior cingulate, posterior parietal cortex, frontal operculum, and ventral insula/caudal OFC were also more active in vanilla + salty minus incongruent, suggesting that delivery of an unfamiliar taste–odor combination may lead to suppressed neural responses. Taken together with previous findings in the literature, these results suggest that the insula, OFC, and ACC are key components of the network underlying flavor perception and that taste–smell integration within these and other regions is dependent on 1) mode of olfactory delivery and 2) previous experience with taste/smell combinations.