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Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons. Artificial intelligence has made major progress in recent years thanks to a technique known as deep learning, which works by mimicking the human brain. When computers employ deep learning, they learn by using networks made up of many layers of simulated neurons. Deep learning has opened the door to computers with human – or even super-human – levels of skill in recognizing images, processing speech and controlling vehicles. But many neuroscientists are skeptical about whether the brain itself performs deep learning. The patterns of activity that occur in computer networks during deep learning resemble those seen in human brains. But some features of deep learning seem incompatible with how the brain works. Moreover, neurons in artificial networks are much simpler than our own neurons. For instance, in the region of the brain responsible for thinking and planning, most neurons have complex tree-like shapes. Each cell has ‘roots’ deep inside the brain and ‘branches’ close to the surface. By contrast, simulated neurons have a uniform structure. To find out whether networks made up of more realistic simulated neurons could be used to make deep learning more biologically realistic, Guerguiev et al. designed artificial neurons with two compartments, similar to the ‘roots’ and ‘branches’. The network learned to recognize hand-written digits more easily when it had many layers than when it had only a few. This shows that artificial neurons more like those in the brain can enable deep learning. It even suggests that our own neurons may have evolved their shape to support this process. If confirmed, the link between neuronal shape and deep learning could help us develop better brain-computer interfaces. These allow people to use their brain activity to control devices such as artificial limbs. Despite advances in computing, we are still superior to computers when it comes to learning. Understanding how our own brains show deep learning could thus help us develop better, more human-like artificial intelligence in the future.

هذا الكتاب ليس فقط به الخطوات الأساسية لمساعدة رواد الأعمال المبتدئين ولكنه أيضا يشجعهم على الابتكار وتطوير مشاريعهم، كما يوضح الكتاب أن هناك نوعين من التقدم، التقدم الأفقي والتقدم العمودي، فالتقدم الأفقي هو الحفاظ على جودة المشروع أو المنتج أو تحسينه، بينما التقدم العمودي هو اتخاذ خطوة للأمام وإنتاج شيء جديد ويؤكد ثقيل على مفهومه موضحا أن من \"الصفر إلى الواحد\" هو الانتقال من لا شيء إلى الواحد بينما من \"الألف إلى الياء\" هو مجرد بناء وتطوير على الأفكار والمنتجات الموجود بالفعل وذلك كناية ودعوة للابتكار والخروج عن المألوف ويقسم ثقيل أفكاره إلى فصول، يحمل كل واحد منها عدة التحذيرات أوعدة نصائح في مجال إدارة الأعمال.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits. The authors propose a synaptic plasticity rule for pyramidal neurons based on postsynaptic bursting that captures experimental data and solves the credit assignment problem for deep networks.

\"Singapore Eurasians: Memories, Hopes and Dreams offers insight into the Singapore Eurasian community, one of Singapore's minority communities. Though small, the Eurasian community has undoubtedly played a big part in Singapore's nation-building. This book is the definitive record of Eurasian history and heritage in Singapore, and serves to educate the younger generation of Eurasians about their roots, the community's achievements and its collective hopes and dreams for the future, as well as provide a useful resource for others to learn more about the Eurasian community. In addition, Singapore Eurasians: Memories, Hopes and Dreams also covers the growth and developments of the Eurasian community within the last 25 years, and how the Eurasian Association (EA), as a Self-Help Group since 1994, has been helping the less fortunate through its programmes, as well as being the main force in driving the preservation and sharing of the Eurasian culture for its future generations. In preserving the history, heritage, hopes and dreams of the Singapore Eurasian community, this book is an effort in contributing to the country's continued multiracial harmony and appreciation of the many elements that make up Singapore's story\"-- Provided by publisher.

Two-dimensional spectroscopic techniques combining terahertz (THz), infrared (IR), and visible pulses offer a wealth of information about coupling among vibrational modes in molecular liquids, thus providing a promising probe of their local structure. However, the capabilities of these spectroscopies are still largely unexplored due to experimental limitations and inherently weak nonlinear signals. Here, through a combination of equilibrium-nonequilibrium molecular dynamics (MD) and a tailored spectrum decomposition scheme, we identify a relationship between the tetrahedral order of liquid water and its two-dimensional IR-IR-Raman (IIR) spectrum. The structure-spectrum relationship can explain the temperature dependence of the spectral features corresponding to the anharmonic coupling between low-frequency intermolecular and high-frequency intramolecular vibrational modes of water. In light of these results, we propose new experiments and discuss the implications for the study of tetrahedrality of liquid water. Direct spectroscopic probes of the impact of structure on dynamical processes in liquids remain scarce. Here, the authors use molecular dynamics simulations to show that the correlation between vibrational coupling and the local tetrahedral structure of liquid water can be studied via hybrid terahertz- and infrared-Raman spectroscopy.

هذا الكتاب ليس فقط به الخطوات الأساسية لمساعدة رواد الأعمال المبتدئين ولكنه أيضا يشجعهم على الابتكار وتطوير مشاريعهم، كما يوضح الكتاب أن هناك نوعين من التقدم، التقدم الأفقي والتقدم العمودي، فالتقدم الأفقي هو الحفاظ على جودة المشروع أو المنتج أو تحسينه، بينما التقدم العمودي هو اتخاذ خطوة للأمام وإنتاج شيء جديد ويؤكد ثقيل على مفهومه موضحا أن من \"الصفر إلى الواحد\" هو الانتقال من لا شيء إلى الواحد بينما من \"الألف إلى الياء\" هو مجرد بناء وتطوير على الأفكار والمنتجات الموجود بالفعل وذلك كناية ودعوة للابتكار والخروج عن المألوف ويقسم ثقيل أفكاره إلى فصول، يحمل كل واحد منها عدة التحذيرات أوعدة نصائح في مجال إدارة الأعمال.

Cyanobacterial harmful algal blooms (cyano HABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude,information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio (USA) respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Sentinel-3 Ocean Land Color Imager (OLCI) data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision making for recreational and drinking water sources.

Lignin, the second most abundant biopolymer found in nature, has emerged as a potential source of sustainable fuels, chemicals, and materials. Finding suitable solvents, as well as technologies for efficient and affordable lignin dissolution and depolymerization, are major obstacles in the conversion of lignin to value-added products. Certain ionic liquids (ILs) are capable of dissolving and depolymerizing lignin but designing and developing an effective IL for lignin dissolution remains quite challenging. To address this issue, the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) model was used to screen 5670 ILs by computing logarithmic activity coefficients ( ln ( γ )) and excess enthalpies ( H E ) of lignin, respectively. Based on the COSMO-RS computed thermodynamic properties ( ln ( γ ) and H E ) of lignin, anions such as acetate, methyl carbonate, octanoate, glycinate, alaninate, and lysinate in combination with cations like tetraalkylammonium, tetraalkylphosphonium, and pyridinium are predicted to be suitable solvents for lignin dissolution. The dissolution properties such as interaction energy between anion and cation, viscosity, Hansen solubility parameters, dissociation constants, and Kamlet–Taft parameters of selected ILs were evaluated to assess their propensity for lignin dissolution. Furthermore, molecular dynamics (MD) simulations were performed to understand the structural and dynamic properties of tetrabutylammonium [TBA] + -based ILs and lignin mixtures and to shed light on the mechanisms involved in lignin dissolution. MD simulation results suggested [TBA] + -based ILs have the potential to dissolve lignin because of their higher contact probability and interaction energies with lignin when compared to cholinium lysinate.

RNA decay is vital for regulating mRNA abundance and gene expression. Existing technologies lack the spatiotemporal precision or transcript specificity to capture the stochastic and transient decay process. We devise a general strategy to inducibly recruit protein factors to modulate target RNA metabolism. Specifically, we introduce a Rapid Inducible Decay of RNA (RIDR) technology to degrade target mRNAs within minutes. The fast and synchronous induction enables direct visualization of mRNA decay dynamics in cells. Applying RIDR to endogenous ACTB mRNA reveals rapid formation and dissolution of RNA granules in pre-existing P-bodies. Time-resolved RNA distribution measurements demonstrate rapid RNA decay inside P-bodies, which is further supported by knocking down P-body constituent proteins. Light and oxidative stress modulate P-body behavior, potentially reconciling the contradictory literature about P-body function. This study reveals compartmentalized RNA decay kinetics, establishing RIDR as a pivotal tool for exploring the spatiotemporal RNA metabolism in cells. Studying RNA decay remains a challenging task. Here, the authors present a technology that enables inducible rapid degradation of targeted mRNAs. Visualizing mRNA decay dynamics unveils insights into P-body function in RNA metabolism.