Mental decline is common, and it's one of the most feared consequences of aging. But cognitive impairment is not inevitable. Here are 12 ways you can help reduce your risk of age-related memory los...

EdgeCortix Dynamic Neural Accelerator IP is efficient, modular, scalable, fully configurable neural network IP for AI in FPGA or SoC designs.

Some, including Tirias Research, even believe that AR devices could be one of the alternative platforms to the rectangular bricks we call smartphones that everyone carries today.

Most excitatory and inhibitory neurotransmitter receptors are concentrated at the postsynaptic density (PSD) facing pre-synaptic terminals containing the corresponding neurotransmitter [1–3]. The preferential accumulation of receptors at synapses is achieved by their specific interactions with a molecular scaffold that links them to the underlying cytoskeleton [4,5]. This scaffold forms the so-called PSD, which acts as a molecular machine and locally controls some aspects of synapse formation, maintenance, plasticity and function. In recent years, the use of single-molecule and real-time imaging has revealed that neurotransmitter receptors are in constant, rapid movement at the neuronal surface and are transiently trapped at PSDs so as to modify the number and composition of receptors that are available to respond to released neurotransmitter [6–8]. Such is the case for the glycine receptor (GlyR), the GABAA receptor (GABAAR), the glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole pro-pionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors, indicating that the phenomenon can be generalized. This dynamic behavior has profoundly modified our view of the synapse. The aim of this chapter is to review some aspects of our knowledge on receptor dynamics.

The function of a neuron is to transmit information within the nervous system. Neural transmission occurs when a neuron is activated, or fired (sends out an ele

A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an “on-and-off” fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.

Abstract. We suggest a method that evaluates the horizontal velocity in the solar photosphere with easily observable values using a combination of neural networ

Nature Communications - In nonlinear tracking control, relevant to robotic applications, the knowledge on the system model may be not available and there is current need in model-free approaches to...

The Dynamic Neural Retraining System (DNRS) and the Gupta Program are two of the main techniques for retraining the brain and healing chronic illness. Here’s my overview and comparison of both programs!

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed. More about PyTorch Installation Binaries From Source Docker Image Building the Documentation Previous Versions Getting Started Communication Releases and Contributing The Team System 2.7 3.5 3.6 Linux CPU — Linux GPU — Windows CPU / GPU — — Linux (ppc64le) CPU — Linux (ppc64le) GPU — See also the ci.pytorch.org HUD. More About PyTorch At a granular level, PyTorch is a library that consists of the following components: Component Description torch a Tensor library like NumPy, with strong GPU support torch.autograd a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch torch.nn a neural networks library deeply integrated with autograd designed for maximum flexibility torch.multiprocessing Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Installation Binaries Commands to install from binaries via Conda or pip wheels are on our website: https://pytorch.org NVIDIA Jetson platforms Python wheels for NVIDIA's Jetson Nano, Jetson TX2, and Jetson AGX Xavier are available via the following URLs: Stable binaries: Python 2.7: https://nvidia.box.com/v/torch-stable-cp27-jetson-jp42 Python 3.6: https://nvidia.box.com/v/torch-stable-cp36-jetson-jp42 Rolling weekly binaries: Python 2.7: https://nvidia.box.com/v/torch-weekly-cp27-jetson-jp42 Python 3.6: https://nvidia.box.com/v/torch-weekly-cp36-jetson-jp42 They requires JetPack 4.2 and above and are maintained by @dusty-nv From Source If you are installing from source, we highly recommend installing an Anaconda environment. If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to are available here Install Dependencies Common conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing On Linux # Add LAPACK support for the GPU if needed conda install -c pytorch magma-cuda90 # or [magma-cuda80 | magma-cuda92 | magma-cuda100 ] depending on your cuda version Get the PyTorch Source git clone --recursive https://github.com/pytorch/pytorch cd pytorch Install PyTorch On Linux export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} python setup.py install On macOS export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"} MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install On Windows At least Visual Studio 2017 Update 3 (version 15.3.3 with the toolset 14.11) and NVTX are needed.

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

A system for bit-serial computation in a neural network is described. The system may be embodied on an integrated circuit and include one or more bit-serial tiles for performing bit- serial computations in which each bit-serial tile receives input neurons and synapses, and communicates output neurons. Also included is an activation memory for storing the neurons and a dispatcher and a reducer. The dispatcher reads neurons and synapses from memory and communicates either the neurons or the synapses bit-serially to the one or more bit-serial tiles. The other of the neurons or the synapses are communicated bit-parallelly to the one or more bit- serial tiles, or according to a further embodiment, may also be communicated bit-serially to the one or more bit-serial tiles. The reducer receives the output neurons from the one or more tiles, and communicates the output neurons to the activation memory.

In the ever-evolving landscape of machine learning and artificial intelligence, staying ahead of the curve is essential. One powerful toolkit that has garnered attention for its flexibility and ease of use is Chainer. This article delves into the world of Chainer, exploring its features, benefits, and how it can be harnessed to build advanced neural

The licensable IP technology will be available as an integrated SoC and can be used for surveillance, vision guided robotics, drones, industrial IoT and more.

BrainChip, a leading provider of ultra-low power, high performance edge AI technology, has been awarded a new patent for dynamic neural function libraries, a key component of its AI processing chip...

Deep Neural Networks (DNNs) have reinvigorated real-world applications that rely on learning patterns of data and are permeating into different industries and markets. Cloud infrastructure and accelerators that offer INFerence-as-a-Service (INFaaS) have become the enabler of this rather quick and invasive shift in the industry. To that end, mostly acceleratorbased INFaaS (Google’s TPU [1], NVIDIA T4 [2], Microsoft Brainwave [3], etc.) has become the backbone of many real-life applications.

Background Clinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go). In contrast, analyses of balance in research settings incorporate quantitative biomechanical measurements (e.g., whole-body angular momentum, H) using motion capture techniques. Fully instrumenting patients in the clinic is not feasible, and thus it is desirable to estimate biomechanical quantities related to balance from measurements taken from a subset of the body segments. Machine learning algorithms are well-suited for this type of low- to high-dimensional mapping. Thus, our goal was to develop and test an artificial neural network that to predict segment contributions to whole-body angular momentum from linear acceleration and angular velocity signals (i.e., those typically available to wearable inertial measurement units, IMUs) taken from a sparse set of body segments. Methods Optical motion capture data were collected from five able-bodied individuals and five individuals with Parkinson's disease (PD) walking on a non-steady-state locomotor circuit comprising stairs, ramps and changes of direction. Motion data were used to calculate angular momentum (i.e., “gold standard” output data) and body-segment linear acceleration and angular velocity data from local reference frames at the wrists, ankles and neck (i.e., network input). A dynamic nonlinear autoregressive neural network was trained using the able-bodied data (pooled across subjects). The neural network was tested on data from individuals with PD with noise added to simulate real-world IMU data. Results Correlation coefficients of the predicted segment contributions to whole-body angular momentum with the gold standard data were 0.989 for able-bodied individuals and 0.987 for individuals with PD. Mean RMS errors were between 2 and 7% peak signal magnitude for all body segments during completion of the locomotor circuits. Conclusion Our results suggest that estimating segment contributions to angular momentum from mechanical signals (linear acceleration, angular velocity) from a sparse set of body segments is a feasible method for assessing coordination of balance—even using a network trained on able-bodied data to assess individuals with neurological disease. These targeted estimates of segmental momenta could potentially be delivered to clinicians using a sparse sensor set (and likely in real-time) in order to enhance balance rehabilitation of people with PD.

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven...

Dynamic neural network (NN) techniques are increasingly important because they facilitate deep learning techniques with more complex network architectures. However, existing studies, which predominantly optimize the static computational graphs by static scheduling methods, usually focus on optimizing static neural networks in deep neural network (DNN) accelerators. We analyze the execution process of dynamic neural networks and observe that dynamic features introduce challenges for efficient scheduling and pipelining in existing DNN accelerators. We propose DyPipe, a holistic approach to optimizing dynamic neural network inferences in enhanced DNN accelerators. DyPipe achieves significant performance improvements for dynamic neural networks while it introduces negligible overhead for static neural networks. Our evaluation demonstrates that DyPipe achieves 1.7x speed up on dynamic neural networks and maintains more than 96% performance for static neural networks.

Tensors and Dynamic neural networks in Python with strong GPU acceleration