An Ultrascalable Solution to Large-scale Neural Tissue Simulation

نویسندگان

  • James Kozloski
  • John Wagner
چکیده

Neural tissue simulation extends requirements and constraints of previous neuronal and neural circuit simulation methods, creating a tissue coordinate system. We have developed a novel tissue volume decomposition, and a hybrid branched cable equation solver. The decomposition divides the simulation into regular tissue blocks and distributes them on a parallel multithreaded machine. The solver computes neurons that have been divided arbitrarily across blocks. We demonstrate thread, strong, and weak scaling of our approach on a machine with more than 4000 nodes and up to four threads per node. Scaling synapses to physiological numbers had little effect on performance, since our decomposition approach generates synapses that are almost always computed locally. The largest simulation included in our scaling results comprised 1 million neurons, 1 billion compartments, and 10 billion conductance-based synapses and gap junctions. We discuss the implications of our ultrascalable Neural Tissue Simulator, and with our results estimate requirements for a simulation at the scale of a human brain.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Decentralized Adaptive Control of Large-Scale Non-affine Nonlinear Time-Delay Systems Using Neural Networks

In this paper, a decentralized adaptive neural controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, non-affine subsystems and unknown nonlinear time-delay interconnections. The stability of the closed loop system is guaranteed through Lyapunov-Krasovskii stability analysis. Simulation results are provided to show the effectiveness of the proposed approache...

متن کامل

پیش‌‌بینی کوتاه مدت قیمت تراکم گرهی در یک سیستم قدرت بزرگ تجدید ساختار یافته با استفاده از شبکه‌های عصبی مصنوعی با بهینه‌سازی آموزش ژنتیکی

In a daily power market, price and load forecasting is the most important signal for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization levenberg-marquardt back propagation (LMBP) training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algo...

متن کامل

An Effective Solution to Large-scale Traveling Salesman Problems Using Chaotic Neural Networks

This paper presents an effective TSP (Traveling Salesman Problem) solver for large-scale problems using neural networks. Firstly, in the proposed method, an intractable large-scale TSP is divided into some tractable small-scale problems (clusters) using a clustering technique. Secondly, a visiting order of clusters is determined using chaotic neural networks (CNNs). Thirdly, the small-scale pro...

متن کامل

Dynamic Performance Analysis and Simulation of a Full Scale Activated Sludge System Treating an Industrial Wastewater Using Artificial Neural Network

Due to changeable nature of the industrial wastewaters, proper operation of an industrial wastewater treatment plant is of prior importance in order to keep the process stability at the desired conditions. In this mean, simulation of the treatment system behavior using artificial neural network (ANN) can be an effective tool.  This paper evaluates long term performance and process stability of ...

متن کامل

A Solution to the Problem of Extrapolation in Car Following Modeling Using an online fuzzy Neural Network

Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree le...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره 5  شماره 

صفحات  -

تاریخ انتشار 2011