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The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains. 

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability in the world and the strongest environmental risk factor for the development of dementia. Even mild TBI (resulting from concussive injuries) is associated with a greater than twofold increase in the risk of dementia onset. Little is known about the cellular mechanisms responsible for the progression of long-lasting cognitive deficits. The integrated stress response (ISR), a phylogenetically conserved pathway involved in the cellular response to stress, is activated after TBI, and inhibition of the ISR—even weeks after injury—can reverse behavioral and cognitive deficits. However, the cellular mechanisms by which ISR inhibition restores cognition are unknown. Here, we used longitudinal two-photon imaging in vivo after concussive injury in mice to study dendritic spine dynamics in the parietal cortex, a brain region involved in working memory. Concussive injury profoundly altered spine dynamics measured up to a month after injury. Strikingly, brief pharmacological treatment with the drug-like small-molecule ISR inhibitor ISRIB entirely reversed structural changes measured in the parietal cortex and the associated working memory deficits. Thus, both neural and cognitive consequences of concussive injury are mediated in part by activation of the ISR and can be corrected by its inhibition. These findings suggest that targeting ISR activation could serve as a promising approach to the clinical treatment of chronic cognitive deficits after TBI. 

Visual perception in natural environments depends on the ability to focus on salient stimuli while ignoring distractions. This kind of selective visual attention is associated with gamma activity in the visual cortex. While the nucleus reticularis thalami (nRT) has been implicated in selective attention, its role in modulating gamma activity in the visual cortex remains unknown. Here, we show that somatostatin- (SST) but not parvalbumin-expressing (PV) neurons in the visual sector of the nRT preferentially project to the dorsal lateral geniculate nucleus (dLGN), and modulate visual information transmission and gamma activity in primary visual cortex (V1). These findings pinpoint the SST neurons in nRT as powerful modulators of the visual information encoding accuracy in V1 and represent a novel circuit through which the nRT can influence representation of visual information. 

Abstract Understanding the connectivity of reef organisms is important to assist in the conservation of biological diversity and to facilitate sustainable fisheries in these ecosystems. Common methods to assess reef connectivity include both population genetics and biophysical modelling. Individually, these techniques can offer insight into population structure; however, the information acquired by any singular analysis is often subject to limitations, underscoring the need for a multi-faceted approach. To assess the connectivity dynamics of the red grouper (Epinephelus morio), an economically important reef fish species found throughout the Gulf of Mexico and USA western Atlantic, we utilized two sets of genetic markers (12 microsatellite loci and 632 single nucleotide polymorphisms) to resolve this species’ population genetic structure, along with biophysical modelling to deliver a spatial forecast of potential larval “sources” and “sinks” across these same regions and spatial scale. Our genetic survey indicates little, if any, evidence of population genetic structure and modelling efforts indicate the potential for ecological connectivity between sampled regions over multiple generations. We offer that using a dual empirical and theoretical approach lessens the error associated with the use of any single method and provides an important step towards the validation of either of these methodologies. 

Assessments of the mouse visual system based on spatial-frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles per degree. However, visually mediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a stimulus class—visual flow patterns—that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually mediated responses well beyond those predicted by spatial-frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15–1.6 cycles per degree), many are orientation or direction selective, and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and expand our understanding of the mouse’s visual capacity to behaviorally relevant ranges. 

A bstract Jet fragmentation transverse momentum ( j T ) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at $$ \sqrt{s_{\mathrm{NN}}} $$ s NN = 5 . 02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti- k T algorithm with resolution parameter R = 0 . 4 in the pseudorapidity range |η| < 0 . 25. The j T values are calculated for charged particles inside a fixed cone with a radius R = 0 . 4 around the reconstructed jet axis. The measured j T distributions are compared with a variety of parton-shower models. Herwig and P ythia 8 based models describe the data well for the higher j T region, while they underestimate the lower j T region. The j T distributions are further characterised by fitting them with a function composed of an inverse gamma function for higher j T values (called the “wide component”), related to the perturbative component of the fragmentation process, and with a Gaussian for lower j T values (called the “narrow component”), predominantly connected to the hadronisation process. The width of the Gaussian has only a weak dependence on jet transverse momentum, while that of the inverse gamma function increases with increasing jet transverse momentum. For the narrow component, the measured trends are successfully described by all models except for Herwig. For the wide component, Herwig and PYTHIA 8 based models slightly underestimate the data for the higher jet transverse momentum region. These measurements set constraints on models of jet fragmentation and hadronisation.