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Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. Many neuroscience experiments today involve using electrodes to record from the brain of an animal, such as a mouse or a monkey, while the animal performs a task. The goal of such experiments is to understand how a particular brain region works. However, modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously. Analysing such large amounts of data then becomes a challenge in itself. This is particularly true for brain regions such as the prefrontal cortex that are involved in the cognitive processes that allow an animal to acquire knowledge. Individual neurons in the prefrontal cortex encode many different types of information relevant to a given task. Imagine, for example, that an animal has to select one of two objects to obtain a reward. The same group of prefrontal cortex neurons will encode the object presented to the animal, the animal’s decision and its confidence in that decision. This simultaneous representation of different elements of a task is called a ‘mixed’ representation, and is difficult to analyse. Kobak, Brendel et al. have now developed a data analysis tool that can ‘demix’ neural activity. The tool breaks down the activity of a population of neurons into its individual components. Each of these relates to only a single aspect of the task and is thus easier to interpret. Information about stimuli, for example, is distinguished from information about the animal’s confidence levels. Kobak, Brendel et al. used the demixing tool to reanalyse existing datasets recorded from several different animals, tasks and brain regions. In each case, the tool provided a complete, concise and transparent summary of the data. The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice. At a technical level, the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future.

IN THE ADULT BRAIN, NEW NEURONS ARE ADDED TO TWO BRAIN AREAS: the olfactory bulb (OB) and the hippocampus. Newly-generated neurons integrate into the preexisting circuits, bringing a set of unique properties, such as increased plasticity and responsiveness to stimuli. However, the functional implications of the constant addition of these neurons remain unclear, although they are believed to be important for learning and memory. The levels of neurogenesis are regulated by a variety of environmental factors, as well as during learning, suggesting that new neurons could be important for coping with changing environmental demands. Notably, neurogenesis has been shown to be physiologically regulated in relation to reproductive behavior: neurogenesis increases in female mice upon exposure to cues of the mating partners, during pregnancy and lactation, and in male mice upon exposure to their offspring. In this scenario, and because of the key contribution of olfaction to maternal behavior, we sought to investigate the contribution of adult-generated neurons in the olfactory system to maternal behavior and offspring recognition. To do so, we selectively disrupted neurogenesis in the olfactory pathway of female mice using focal irradiation. Disruption of adult neurogenesis in the OB did not affect maternal behavior, or the ability of female mice to discriminate familiar from unfamiliar pups. However, reduction of olfactory neurogenesis resulted in abnormal social interaction of female mice, specifically with male conspecifics. Because the olfactory system is crucial for sex recognition, we suggest that the abnormal interaction with males could result from the inability to detect or discriminate male-specific odors and could therefore have implications for the recognition of potential mating partners. Here, I review the results of our study and others, and discuss their implications for our understanding of the function of adult neurogenesis.

Adult-born neurons arrive to the olfactory bulb (OB) and integrate into the existing circuit throughout life. Despite the prevalence of this phenomenon, its functional impact is still poorly understood. Recent studies point to the importance of newly generated neurons to olfactory learning and memory. Adult neurogenesis is regulated by a variety of factors, notably by instances related to reproductive behavior, such as exposure to mating partners, pregnancy and lactation, and exposure to offspring. To study the contribution of olfactory neurogenesis to maternal behavior and social recognition, here we selectively disrupted OB neurogenesis using focal irradiation of the subventricular zone in adult female mice. We show that reduction of olfactory neurogenesis results in an abnormal social interaction pattern with male, but not female, conspecifics; we suggest that this effect could result from the inability to detect or discriminate male odors and could therefore have implications for the recognition of potential mating partners. Disruption of OB neurogenesis, however, neither impaired maternal-related behaviors, nor did it affect the ability of mothers to discriminate their own progeny from others.

In many brain areas, neuronal activity is associated with a variety of behavioral and environmental variables. In particular, neuronal responses in the zebrafish hindbrain relate to oculomotor and swimming variables, as well as sensory information. However, the precise functional organization of the neurons has been difficult to unravel since neuronal responses are heterogeneous. Here we used dimensionality reduction methods on neuronal population data to reveal the role of the hindbrain in visually-driven oculomotor behavior and swimming. We imaged neuronal activity in zebrafish expressing GCaMP6s in the nucleus of almost all neurons, while monitoring the behavioral response to gratings that rotated with different speeds. We then used reduced-rank regression, a method that condenses the sensory and motor variables into a smaller number of ′features′, to predict the fluorescence traces of all ROIs (regions of interest). Despite the potential complexity of the visuo-motor transformation, our analysis revealed that the bulk of population activity can be explained by only two features. Based on the contribution of these features to each ROI′s activity, ROIs formed three clusters. One cluster was related to vergent movements and swimming, whereas the other two clusters related to leftward and rightward rotation. Voxels corresponding to these clusters were segregated anatomically, with leftward and rightward rotation clusters located selectively to the left and right hemispheres, respectively. Just as described in many cortical areas, our analysis revealed that single neuron complexity co-exists with a simpler population-level description, thereby providing insights into the organization of visuo-motor transformations in the hindbrain.Competing Interest StatementThe authors have declared no competing interest.

Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often obscures the represented information. Here we introduce a novel dimensionality reduction technique, demixed principal component analysis (dPCA), which automatically discovers and highlights the essential features in complex population activities. We reanalyze population data from the prefrontal areas of rats and monkeys performing a variety of working memory and decision-making tasks. In each case, dPCA summarizes the relevant features of the population response in a single figure. The population activity is decomposed into a few demixed components that capture most of the variance in the data and that highlight dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc. Moreover, dPCA reveals strong, condition-independent components of the population activity that remain unnoticed with conventional approaches.