Species-specific mitochondria dynamics and metabolism regulate the timing of neuronal development.

The evolution of species involves changes in the timeline of key developmental programs. Among these, neuronal development is considerably prolonged in the human cerebral cortex compared with other mammals, leading to brain neoteny. Here we explore whether mitochondria influence the species-specific properties of cortical neuron maturation. By comparing human and mouse cortical neuronal maturation at high temporal and cell resolution, we found a slower pattern of mitochondria development in human cortical neurons compared with the mouse, together with lower mitochondria metabolic activity, particularly oxidative phosphorylation. Stimulation of mitochondria metabolism in human neurons resulted in accelerated maturation, leading to excitable and complex cells weeks ahead of time. Our data identify mitochondria as important regulators of the pace of neuronal development underlying human-specific features of brain evolution.

Living Cells, Lifeless Molecules

Cells are composed of molecules that are lifeless but special, because most of them occur in nature only in the context of life. They are essential to all the workings of life, and no one single class holds life’s secret: life is an emergent property of the collective of molecules, assembled into the elaborate structures called cells. Cells come in great profusion, but all are variations on just two patterns of organization: prokaryotes, small and relatively simple microbes, both Bacteria and Archaea; and eukaryotes (Eukarya), the larger and more complex cells that make up all animals, plants, and fungi. The molecules of life, for all their diversity, again fall mainly into just a handful of categories. The bulk of living matter consists of proteins, nucleic acids, carbohydrates and lipids. Biomolecules belong to chemistry, but their functions in the process of living place them in the realm of biology.

Essentiality of Sis1, a J-domain protein Hsp70 cochaperone, can be overcome by Tti1, a specialized PIKK chaperone

J-domain protein cochaperones drive much of the functional diversity of Hsp70-based chaperone systems. Sis1 is the only essential J-domain protein of the cytosol/nucleus of Saccharomyces cerevisiae. Why it is required for cell growth is not understood, nor is how critical its role in regulation of heat shock transcription factor 1 (Hsf1). We report that single residue substitutions in Tti1, a component of the heterotrimeric TTT complex, a specialized chaperone system for phosphatidylinositol 3-kinase-related kinase (PIKK) proteins, allow growth of cells lacking Sis1. Upon depletion of Sis1, cells become hypersensitive to rapamycin, a specific inhibitor of TORC1 kinase. In addition, levels of the three essential PIKKs (Mec1, Tra1, and Tor2), as well as Tor1, decrease upon Sis1depletion. Overexpression of Tti1 allows growth, without an increase in the other subunits of the TTT complex, Tel2 and Tti2, suggesting that it can function independent of the complex. Cells lacking Sis1, with viability supported by Tti1 suppressor, substantially upregulate some, but not all, heat shock elements activated by Hsf1. Together, our results suggest that Sis1 is required as a cochaperone of Hsp70 for the folding/maintenance of PIKKs making Sis1 an essential gene, and its requirement for Hsf1 regulation is more nuanced than generally appreciated.

New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.

Normalization by orientation anisotropy in human V1-V3

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a two-stage computation. The first stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of linear filters in quadrature phase. The second stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet traditional energy models, including normalized energy models, predict responses that are about the same. Here, we propose a computational model, in which responses are normalized not by the sum of the contrast energy, but by the orientation anisotropy, computed as the variance in contrast energy across orientation channels. We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We speculate that high anisotropy in the orientation responses leads to larger outputs in downstream areas, which in turn normalizes responses in these later visual areas, as well as in V1 via feedback.

Anatomical Study of Some Species of the Brassicaceae Family

Study complete in postgraduate laboratories at the College of Science, Department of Biology, University of Kufa. The anatomical study revealed a variation in the formation of complex cells and epidermal cells, where they differed in their shapes and were irregular with wavy ridges and on the axial surfaces in R. sativusvar red L. and R. sativuslongipinntus L. Their walls were weak jagged in only R. raphenstrum. Also, the stomata were located on both sides of the leaf blade, and the upper epidermis contained fewer stomata compared to the lower epidermis.

Generative perspective of the primary visual cortex

According to analysis-by-synthesis theories of perception, the primary visual cortex (V1) reconstructs visual stimuli through top-down pathway, and higher-order cortex reconstructs V1 activity. Experiments also found that neural representations are generated in a top-down cascade during visual imagination. What code does V1 provide higher-order cortex to reconstruct or simulate to improve perception or imaginative creativity? What unsupervised learning principles shape V1 for reconstructing stimuli so that V1 activity eigenspectrum is power-law with close-to-1 exponent? Using computational models, we reveal that reconstructing the activities of V1 complex cells facilitate higher-order cortex to form representations smooth to shape morphing of stimuli, improving perception and creativity. Power-law eigenspectrum with close-to-1 exponent results from the constraints of sparseness and temporal slowness when V1 is reconstructing stimuli, at a sparseness strength that best whitens V1 code and makes the exponent most insensitive to slowness strength. Our results provide fresh insights into V1 computation.

Pooling in a predictive model of V1 explains functional and structural diversity across species

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of cortical orientation maps in higher mammals' V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a model of V1 based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence of complex cells as well as cortical orientation maps in V1, as observed in distinct species of mammals. By using different pooling functions, our model developed complex cells in networks that exhibit orientation maps (e.g., like in carnivores and primates) or not (e.g., rodents and lagomorphs). The SDPC can therefore be viewed as a unifying framework that explains the diversity of structural and functional phenomena observed in V1. In particular, we show that orientation maps emerge naturally as the most cost-efficient structure to generate complex cells under the predictive coding principle.