The dentate gyrus (DG) controls information flow into the hippocampus and is critical for learning, memory, pattern separation, and spatial coding, while DG dysfunction is associated with neuropsychiatric disorders. Despite its importance, the molecular mechanisms regulating DG neural circuit assembly and function remain unclear. Here, we identify the Rac-GEF Tiam1 as an important regulator of DG development and associated memory processes. In the hippocampus, Tiam1 is predominantly expressed in the DG throughout life. Global deletion of Tiam1 in male mice results in DG granule cells with simplified dendritic arbors, reduced dendritic spine density, and diminished excitatory synaptic transmission. Notably, DG granule cell dendrites and synapses develop normally in Tiam1 KO mice, resembling WT mice at postnatal day 21 (P21), but fail to stabilize, leading to dendrite and synapse loss by P42. These results indicate that Tiam1 promotes DG granule cell dendrite and synapse stabilization late in development. Tiam1 loss also increases the survival, but not the production, of adult-born DG granule cells, possibly because of greater circuit integration as a result of decreased competition with mature granule cells for synaptic inputs. Strikingly, both male and female mice lacking Tiam1 exhibit enhanced contextual fear memory and context discrimination. Together, these results suggest that Tiam1 is a key regulator of DG granule cell stabilization and function within hippocampal circuits. Moreover, based on the enhanced memory phenotype of Tiam1 KO mice, Tiam1 may be a potential target for the treatment of disorders involving memory impairments. SIGNIFICANCE STATEMENT The dentate gyrus (DG) is important for learning, memory, pattern separation, and spatial navigation, and its dysfunction is associated with neuropsychiatric disorders. However, the molecular mechanisms controlling DG formation and function remain elusive. By characterizing mice lacking the Rac-GEF Tiam1, we demonstrate that Tiam1 promotes the stabilization of DG granule cell dendritic arbors, spines, and synapses, whereas it restricts the survival of adult-born DG granule cells, which compete with mature granule cells for synaptic integration. Notably, mice lacking Tiam1 also exhibit enhanced contextual fear memory and context discrimination. These findings establish Tiam1 as an essential regulator of DG granule cell development, and identify it as a possible therapeutic target for memory enhancement.
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that models based on convolutional neural networks (CNNs) lead to much more accurate predictions, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance. Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations. We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations. We fit this model to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging. We show that our rotation-equivariant network not only outperforms a regular CNN with the same number of feature maps, but also reveals a number of common features shared by many V1 neurons, which deviate from the typical textbook idea of V1 as a bank of Gabor filters. Our findings are a first step towards a powerful new tool to study the nonlinear computations in V1.
These are data tables released as part of the MICrONS project, and contain annotations on the EM data and reconstructions. You can find more information about the dataset and the project at microns-explorer.org.
Abstract Despite variations in appearance we robustly recognize objects. Neuronal populations responding to objects presented under varying conditions form object manifolds and hierarchically organized visual areas untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of object manifolds along the cortex remain unknown. Using home cage training we showed that mice are capable of invariant object recognition. We simultaneously recorded the responses of thousands of neurons to measure the information about object identity across the visual cortex and found that lateral areas LM, LI and AL carry more linearly decodable object information compared to other visual areas. We applied the theory of linear separability of manifolds, and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying the key features in the geometry of the population neural code that enable invariant object coding.
Summary A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy calcium fluorescence traces. We systematically evaluate a range of spike inference algorithms on a large benchmark dataset (>100.000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). We introduce a new algorithm based on supervised learning in flexible probabilistic models and show that it outperforms all previously published techniques. Importantly, it even performs better than other algorithms when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can easily be used to further improve its spike prediction accuracy and generalization performance. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmark datasets such as the one we provide may greatly facilitate future algorithmic developments.
In primates and most carnivores, neurons in primary visual cortex are spatially organized by their functional properties. For example, neurons with similar orientation preferences are grouped together in iso-orientation domains that smoothly vary over the cortical sheet. In rodents, on the other hand, neurons with different orientation preferences are thought to be spatially intermingled, a feature which has been termed “salt-and-pepper” organization. The apparent absence of any systematic structure in orientation tuning has been considered a defining feature of the rodent visual system for more than a decade, with broad implications for brain development, visual processing, and comparative neurophysiology. Here, we revisited this question using new techniques for wide-field two-photon calcium imaging that enabled us to collect nearly complete population tuning preferences in layers 2-4 across a large fraction of the mouse visual hierarchy. Examining the orientation tuning of these hundreds of thousands of neurons, we found a global map spanning multiple visual cortical areas in which orientation bias was organized around a single pinwheel centered in V1. This pattern was consistent across animals and cortical depth. The existence of this global organization in rodents has implications for our understanding of visual processing and the principles governing the ontogeny and phylogeny of the visual cortex of mammals.
