38, No. The invention relates to a modular architecture of a cellular network for improved large-scale integration, of the type which comprises a plurality of fuzzy cellular elements (C m ,n) interconnected to form a matrix of elements having at least m rows and n columns, the row and column numbers describing the location of each element. This way, a modular neural network can perform a highly complex task with much higher efficiency. 5) Modular Neural Network. Neuronal network architecture is not based on a genetic blueprint alone but is shaped by predefined rules of activity-dependent self-organization (Spitzer, 2006). An Analog Neural Computer with Modular Architecture for Real-Time Dynamic Computations Abstract The paper describes a multichip analog parallel neural network whose architecture, neuron characteristics, synaptic connections, and time constants are modifiable. 2. INTRODUCTION Many seemingly complex tasks in the brain can be divided into subtasks [8]. Jacobs et al. The work presented in this article circumvents these problems by the use of modular architecture (“divide and conquer” strategy) Abstract: Deep neural networks have been adopted in a wide range of application domains, leading to high demand for inference accelerators. In general, combined networks are more powerful than flat unstructured ones. A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary. Neural Architecture Search. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform. As … They all perform specific tasks, but they do not interact with each other during the computation process. monolithic system, i.e., a system with only one neural network that receives and analyses all available information, resulting in conflicting training patterns, long training times and poor generalization. Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerard; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam 2013-01-01 00:00:00 Neural … In order to overcome the drawbacks of the inverse kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the inverse kinematics model learning. Based on this flexible architecture, networks with high numbers of inputs and The architecture's networks compete to learn the training patterns. PathNet is a new Modular Deep Learning (DL) architecture, ... (AGI) it would be efficient if multiple users trained the same giant neural network, … A Competitive Modular Connectionist Architecture 769 the ith output unit of the gating network, denoted gi, is eS , gi = ~n~-2:es; j=l (1) where Si denotes the weighted sum of unit i's inputs and n denotes the number of expert networks. [6] M. Olsen-Darter and V. Gordon, Vehicle Steering Control Using Modular Neural Networks, IEEE International Conference on Information Reuse and Integration (IRI), pp 374-379, 2005. The proposed architecture is interpreted as a generalized nite mixture model and, therefore, parameter estimation is treated as a Most works are based on searching CNN architectures for image classification while only a few of them [8,30,10] focus on more complicated vision tasks such as semantic In this approach, a modular neural network is treated as a phe-notype of an individual, and the modular architecture is op-timized through the evolution of its genetic representation (genotype) by using genetic algorithms. A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. Therefore we pro- pose a novel modular neural network architecture for the inverse kinematics model learning and online incremental learning method for the architecture. CiteSeerX - Scientific articles matching the query: A Modular Neural Network Architecture for Rainfall Estimation. Overview. Modular neural networks, as combined structures, have also a biological back-ground: Natural neural systems are composed of a hierarchy of networks built of elements specialized for different tasks. Abstract This paper presents an original modular neural network architecture whose modules are multilayer per-ceptrons. Neural networks that learn the What and Where task perform better if they possess a modular architecture for separately processing the identity and spatial location of objects. Classification capacity of a modular neural network implementing neurally inspired architecture and training rules. Its multilayer proposed a modular network architecture that consists of a number of expert networks and a gating network[l] [2]. 1 for B,D,G) as an approach to phoneme discrimination that achieves very high recognition scores [Waibel 89, Waibel 88a]. (iii) MAGNet Tuner, a design space exploration framework encompassing the designer, the mapper, and a deep learning framework to enable fast design space exploration and co-optimization of architecture and application. NAS aims at automati-cally finding an efficient neural network architecture for a certain task and dataset without labor of designing network. design of optimal modular neural network architectures. 2 Enhancement of neural representation capacity by modular architecture in networks of cortical neurons Ofri Levy, Noam E. Ziv and Shimon Marom Faculty of Medicine and Network Biology Laboratories, Technion, Haifa, Israel Keywords: classification, modularity, multi-electrode array, neural network, representation scheme Abstract 1 January 2013 | Neural Processing Letters, Vol. In fact, these principles may be found extremely useful for those who plan to implement a neural architecture … Small Phonemic Classes by Time-Delay Neural Networks In our previous work, we have proposed a Time-Delay Neural Network architecture (as shown on the left of Fig. Poirazi P(1), Neocleous C, Pattichis CS, Schizas CN. executing a neural network on the generated hardware. Modular Weightless Neural Network Architecture for Intelligent Navigation Siti Nurmaini, Siti Zaiton Mohd Hashim, Dayang Norhayati Abang Jawawi Faculty of Computer Science University of Sriwijaya Indonesia e-mail: siti_nurmaini@.unsri.ac.id Faculty of Computer Science and Information System Universiti Teknologi Malaysia Skudai, Johor Bahru The system has several important features, such as We demonstrate the utility of MAGNet by designing an inference accelerator As a result, a plant's The modules’ inputs are external inputs or hidden layers of other modules, thereby allowing them to be connected in a general manner. The subsumption architecture is a layered application organisation, used to partition high level behavioral robotic applications into layers of control modules, where Using the given question, the model assembles a concrete network architecture and then executes the assembled neural module network to output an answer which is shown in Figure 3 and 4. The authors describe a multinetwork, or modular, neural network architecture that learns to perform control tasks using a piecewise control strategy. Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network Sandeep Pande, Fearghal Morgan, Seamus Cawley, Tom Bruintjes and Gerard Smit et al. To lower design cost, we propose MAGNet, a modular accelerator generator for neural networks. 3: Modular Neural Network Execution Architecture architecture has been developed based on the MNN design concepts [12]. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Modular Neural Network, Multiple Gradients, Explain-able Neural Networks, Neural Network Architecture 1. Figure 3: For each instance, the model first predicts a layout, then using the image features, it executes the assembled neural module network to output an answer. sisting of a single neural network. Inverse Kinematics Learning by Modular Architecture Neural Networks with Performance Prediction Networks Eimei OYAMA and Nak Young Chong Arvin Agah ... A modular neural network architecture was pro-posed by Jacobs et al. Neural Module Fig. An alternative modular neural network architecture is proposed to provide a more e ective implementation of soft competition on di erent feature sets. Each fuzzy processor is adapted for … The combined network can growgradually and can be made fault-tolerant. It is descried how this concept is deployed in natural neural networks on an architectural as well as on a functional level. Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer Coline Devin1 Abhishek Gupta1 Trevor Darrell1 Pieter Abbeel1 Sergey Levine1 Abstract—Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representa- Published in: IJCNN'99. The intermediary takes the outputs of each module and processes them to … This paper focuses on the powerful concept of modularity. The output of the entire architecture, denoted y, is n This network possesses several networks that function independently. ART and Modular Neural Network Architecture for Multilevel Categorization and Recognition of Fingerprints @article{Gour2010ARTAM, title={ART and Modular Neural Network Architecture for Multilevel Categorization and Recognition of Fingerprints}, author={Bhupesh Gour and T. K. … The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other … DOI: 10.1109/WKDD.2010.19 Corpus ID: 18642518. However, the high cost associated with ASIC hardware design makes it challenging to build custom accelerators for different targets. Herein, neuronal migration (Komuro and Kumada, 2005; Zheng and Poo, 2007) and neurite outgrowth (Kater et al., 1988) are regulated by activity-related changes of [Ca 2+] i. [5] K. Chen, L. Yang, X. Yu, and H. Chi, A Self-Generating Modular Neural Network Architecture for Supervised Learning, Neurocomputing 16: 33-48, 1997. In addition, the current paper emphasizes important FPGA design principles, which turn the development of a neural network into a much more modular procedure. Modularity in the brain is thought to give the … To test the predictions of the brain’s modular functional architecture with connector nodes, we built a network model of the brain by measuring spontaneous neural activity with rs-fMRI and correlated the activity probabilities during each BrainMap task [i.e., how often a region’s blood oxygenation level-dependent (BOLD) activation magnitude was high enough to be … and has been used by many researches [3][4][5].