Pattern memory analysis based on stability theory of cellular neural networks

AbstractIn this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples

Modeling multiple object scenarios for feature recognition and classification using cellular neural networks

Cellular neural networks (CNNs) have been adopted in the spatio-temporal processing research field as a paradigm of complexity. This is due to the ease of designs for complex spatio-temporal tasks introduced by these networks. This has led to an increase in the adoption of CNNs for on-chip VLSI implementations. This dissertation proposes the use of a Cellular Neural Network to model, detect and classify objects appearing in multiple object scenes. The algorithm proposed is based on image scene enhancement through anisotropic diffusion; object detection and extraction through binary edge detection and boundary tracing; and object classification through genetically optimised associative networks and texture histograms. The first classification method is based on optimizing the space-invariant feedback template of the zero-input network through genetic operators, while the second method is based on computing diffusion filtered and modified histograms for object classes to generate decision boundaries that can be used to classify the objects. The primary goal is to design analogic algorithms that can be used to perform these tasks. While the use of genetically optimized associative networks for object learning yield an efficiency of over 95%, the use texture histograms has been found very accurate though there is a need to develop a better technique for histogram comparisons. The results found using these analogic algorithms affirm CNNs as well-suited for image processing tasks

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods

Split and Shift Methodology: Overcoming Hardware Limitations on Cellular Processor Arrays for Image Processing

Na era multimedia, o procesado de imaxe converteuse nun elemento de singular importancia nos dispositivos electrónicos. Dende as comunicacións (p.e. telemedicina), a seguranza (p.e. recoñecemento retiniano) ou control de calidade e de procesos industriais (p.e. orientación de brazos articulados, detección de defectos do produto), pasando pola investigación (p.e. seguimento de partículas elementais) e diagnose médica (p.e. detección de células estrañas, identificaciónn de veas retinianas), hai un sinfín de aplicacións onde o tratamento e interpretación automáticas de imaxe e fundamental. O obxectivo último será o deseño de sistemas de visión con capacidade de decisión. As tendencias actuais requiren, ademais, a combinación destas capacidades en dispositivos pequenos e portátiles con resposta en tempo real. Isto propón novos desafíos tanto no deseño hardware como software para o procesado de imaxe, buscando novas estruturas ou arquitecturas coa menor area e consumo de enerxía posibles sen comprometer a funcionalidade e o rendemento

Non-Uniform Cellular Neural Network and its Applications

セルラーニューラルネットワーク(CNN)には連続時間的な ものと，離散時間的なものがあり，本研究は主に後者について議論 する. CNNは1988年にカリフォルニア大学バークレ校のL.O.Chua 教授らによって提案され，現在，アメリカ，ヨーロッパを中心に盛 んに研究が進められている. CNNは従来のニューラルネットワー クと異なり，近傍のセルとのみ結合しているため集積回路としての 実現が容易であり，画像処理用CNNとして注目されている. 第一章では，ニューラルネットワークに関する研究の動向，お よび，人間の目と同様な処理機能を持つ連続時間CNNに関する研 究の動向と，この論文で議論している離散時間CNNの背景につい て簡単に述べている.　 第二章では，離散時間的な非均一CNNとして，二相同期信号 の回路モデルを提案し，その安定性等について議論してある. この モデルは各セルについて二相同期信号1個で実現できるため，VLS1 の実現が容易であると云う特徴がある. まず，モデルの動作原理か ら状態電圧，出力電圧の動作領域を明かにした. このことは物理的 に実現可能なCNNを設計するために重要である.つぎに，安定性 を議論するためにエネルギ一関数からリアフノフ関数を定義し，そ の関数の時間単調減少の条件を利用して，大域的な安定性を持つ離 散時間CNNの設計方法を明らかにした. 第三章では，非線形システムにおける平衡点の求解法について 議論している.連想記憶に用いられるCNNは多くの平衡点をもち， 入力信号によってどの平衡点に到達するかが決定せられる. ロバス トな連想記憶用CNNを設計するためには，このような平衡点を調 べることが必要である. ここでは，解曲線追跡法に基づいた複数解 の求解アルゴリズムを提案している. このアルゴリズムは急激な解 曲線の変化を効率よく追跡できるように，エルミー卜予測子とBDF 積分公式に基づいている. また，大規模系に適用できるようにニュ ートン・ラフソン法の代わりにブラウンの反復法を採用している. このようなアルゴリズを採用することによりロバストなCNNの設 計が可能となる. 第四章では， 離散時間CNNによる連想記憶について述べてい る. 連想記憶は人間の脳の基本的な機能であり，ニューラルネット ワーク応用研究の一つとして古くから盛んに研究されている.本章 では， 離散的なCNNを用いた外積学習アルゴリズムと中点写像ア ルゴリズムの2種類の記憶方式を提案し，その性質を解明している. まず，前者は，入力パターンに対して，エネルギー関数の値が最少 になるようにニューロン間の接続を表す重み行列を設定しようと云 うものであり，これはHebbの理論に基づいている. また，上のよう な手法で学習されたパターンを連想記憶できる条件について議論し た.中点写像アルゴリズムは重み行列の設定方法に対して， いま考 えている中心セルからの近傍を定義し，近傍に存在するセルの状態、 をベクトル表示する.これを全てパターンについて実行し，このよ うにして決定された行列によって写像されるセルのパターンが，元 の中心セルと同一のパターンを持つように重み行列を設定しようと いうもので，数学的には一般化逆行列の理論に基づいている.この ような学習方法の特徴は入力された画像が全て連想されると云うこ とである. 本章では，さらに，このことを応用例によって実証した. 第五章では，画像処理への応用として，輪郭抽出，雑音除去， 視覚パターンの認識に対する離散的なCNNについて述べている. 多くの結果から処理時間は従来のものと比較して極端に短縮される ことが分かった. また，不均一離散時間CNNによって，一つ画面 中に多数の異なる視覚パターンを同時に認識できることも分かった。 第六章では，不均一離散的なCNNの特徴と今後の問題点につ いて述べている

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Mechanisms and Function of Neural Synchronization in an Insect Olfactory System

One of the fundamental questions in modem integrative neurobiology relates to the encoding of sensory information by populations of neurons, and to the significance of this activity for perception, learning, memory and behavior. Synchronization of activity across a population of neurons has been observed many times over, but has never been demonstrated to be a necessary component of this coding process. Neural synchronization has been found in many brain areas in animals across several phyla, from molluscs to mammals. Studies in mammals have correlated the degree of neural synchronization with specific behavioral or cognitive states, such as sensorimotor tasks, segmentation and binocular rivalry suggesting a functional link. In the locust olfactory system, oscillatory synchronization is a prominent feature of the odor-evoked neural activity. Stimulation of the antenna by odors evokes synchronized firing in dynamic and odor-specific ensembles of the projection neurons of the antennal lobe, the principal neurons of the first-order olfactory relay in insects. The coherent activity of these projection neurons underlies an odor-evoked oscillatory field potential which can be recorded in the mushroom body, the second-order olfactory relay to which they project. In this dissertation, we investigated two important questions raised by these findings: how are such stimulus-evoked synchronous ensembles generated, and what is their functional significance? To address these questions, we performed electrophysiological experiments and recorded odor responses from neurons of the antennal lobes and mushroom bodies of locusts, in vivo and using natural odor stimulation in an unanesthetized, semi-intact preparation. We demonstrated the critical mechanism involved in neural synchronization of the antennal lobe neurons. The synchronization of the projection neurons relies critically on fast GABA (γ-aminobutyric acid) -mediated inhibition from the local interneurons. Projection neuron synchronization could be selectively blocked by local injection of the GABA receptor antagonist, picrotoxin. Picrotoxin spared the odor-specific, slow modulation of individual projection neuron responses, but desynchronized the firing of the odor-activated projection neuron assemblies. The oscillatory activity of the local intemeurons was also blocked by picrotoxin, which indicates that such activity depends on network synaptic dynamics. We also showed that the mushroom body networks are capable of generating oscillatory behavior of a similar frequency as that of its projection neuron inputs, and that they may thus be "tuned" to accept synchronized, oscillatory inputs of that frequency range. Our understanding of this mechanism, in tum, made possible the functional investigation of neural synchronization by selective disruption of projection neuron synchronization. We studied a population of neurons downstream from the antennal lobe projection neurons, the extrinsic neurons of the β-lobe of the mushroom body (βLNs). These βLNs were chosen for investigation because they were found to be odor-responsive and because their position in the olfactory pathway makes them a suitable "read-out" of population activity in the antennal lobe. We characterized βLN odor responses before and after selective disruption of the synchronization of the projection neuron ensembles with local picrotoxin injection into the antennal lobe. We showed that the tuning of these βLN responses was altered by PN desynchronization by changing existing responses and inducing new responses. This alteration in tuning resulted in a significant loss of odor specificity in individual βLN responses, an effect that never occurred in the responses of individual, desynchronized projection neurons. We thus propose that neural synchronization is indeed important for information processing in the brain: it serves, at least in part, as a temporal substrate for the transmission of information that is contained across co-activated neurons (relational code) early in the pathway.</p

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs