bluuebunny/crossref_metadata_2025 huggingface dataset:
A Self-Organizing Algorithm for Vector Quantizer Design Applied to Signal Processing
Authors: MADEIRO F. , VILAR R. M. , FECHINE J. M. , NETO B. G. AGUIAR
Published: May 2003

Vector quantization plays an important role in many signal processing problems, such as speech/speaker recognition and signal compression. This paper presents an unsupervised algorithm for vector quantizer design. Although the proposed method is inspired in Kohonen learning, it does not incorporate the classical definition of topological neighborhood as an array of nodes. Simulations are carried out to compare the performance of the proposed algorithm, named SOA (self-organizing algorithm), to that of the traditional LBG (Linde-Buzo-Gray) algorithm. The authors present an evaluation concerning the codebook design for Gauss-Markov and Gaussian sources, since the theoretic optimal performance bounds for these sources, as described by Shannon's Rate-Distortion Theory, are known. In speech and image compression, SOA codebooks lead to reconstructed (vector-quantized) signals with better quality as compared to the ones obtained by using LBG codebooks. Additionally, the influence of the initial codebook in the algorithm performance is investigated and the algorithm ability to learn representative patterns is evaluated. In a speaker identification system, it is shown that the the codebooks designed by SOA lead to higher identification rates when compared to the ones designed by LBG.
Approximation Techniques for Neuromimetic Calculus
Authors: VIGNERON VINCENT , BARRET CLAUDE
Published: May 2003

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.
Unsupervised Learning and Recall of Temporal Sequences: An Application to Robotics
Authors: BARRETO GUILHERME DE A. , ARAÚJO ALUIZIO F.R.
Published: May 2003

This paper describes an unsupervised neural network model for learning and recall of temporal patterns. The model comprises two groups of synaptic weights, named competitive feedforward and Hebbian feedback, which are responsible for encoding the static and temporal features of the sequence respectively. Three additional mechanisms allow the network to deal with complex sequences: context units, a neuron commitment equation, and redundancy in the representation of sequence states. The proposed network encodes a set of robot trajectories which may contain states in common, and retrieves them accurately in the correct order. Further tests evaluate the fault-tolerance and noise sensitivity of the proposed model.
Autonomous Learning Based on Cost Assumptions: Theoretical Studies And Experiments in Robot Control
Authors: RIBEIRO CARLOS H.C. , HEMERLY ELDER M.
Published: May 2003

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic, Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.
Multilayer Neural Networks and Function Reconstruction By Using a Priori Knowledge
Authors: PEDROZA L.C. , PEDREIRA C.E.
Published: May 2003

This paper proposes a new methodology to approximate functions by incorporating a priori information. The relationship between the proposed scheme and multilayer neural networks is explored theoretically and numerically. This approach is particularly interesting for the very relevant class of limited spectrum functions. The number of free parameters is smaller if compared to Back-Propagation Algorithm opening the way for better generalization results.
Knowledge Extraction: A Comparison Between Symbolic and Connectionist Methods
Authors: NOBRE C. , MARTINELI E. , BRAGA A. , CARVALHO A. de , REZENDE S. , BRAGA J. L. , LUDERMIR T.
Published: May 2003

The use of a linguistic representation for expressing knowledge acquired by learning systems is an important issue as regards to user understanding. Under this assumption, and to make sure that these systems will be welcome and used, several techniques have been developed by the artificial intelligence community, under both the symbolic and the connectionist approaches. This work discusses and investigates three knowledge extraction techniques based on these approaches. The first two techniques, the C4.5 [12] and CN2 [6] symbolic learning algorithms, extract knowledge directly from the data set. The last technique, the TREPAN algorithm [10] extracts knowledge from a previously trained neural network. The CN2 algorithm induces if … then rules from a given data set. The C4.5 algorithm extracts decision trees, although it can also extract ordered rules, from the data set. Decision trees are also the knowledge representation used by the TREPAN algorithm.
Biologically Plausible Models of Topographic Map Formation in the Somatosensory and Auditory Cortices
Authors: MAZZA M.B. , DE PINHO M. , Roque A.C.
Published: May 2003

Computational models of the somatosensory and auditory systems have been constructed with the neurosimulator GENESIS. The somatosensory model consists of a cortical layer with 1024 pyramidal cells and 512 basket cells connected to a hand surface with 512 tactile receptors. The auditory model consists of a cortical layer with 2256 pyramidal cells and 1128 basket cells connected to a cochlea with 47 receptors. The models reproduce processes related to the formation and maintenance of somatotopic and tonotopic maps and exhibit several features observed in experiments with animals such as variability in the shapes and sizes of areas of cortical representation and, in the case of somatotopy, cortical magnification values in agreement with experimental findings and linear decay of receptive field overlap as a function of cortical distance between recording Bites in normal conditions.
THE BACKPROPAGATION ALGORITHM IN J, A FAST PROTOTYPING TOOL FOR RESEARCHING NEURAL NETWORKS
Authors: BROUWER ROELOF K.
Published: May 2003

This paper illustrates the use of a powerful language, called J, that is ideal for simulating neural networks. The use of J is demonstrated by its application to a gradient descent method for training a multilayer perceptron. It is also shown how the back-propagation algorithm can be easily generalized to multilayer networks without any increase in complexity and that the algorithm can be completely expressed in an array notation which is directly executable through J. J is a general purpose language, which means that its user is given a flexibility not available in neural network simulators or in software packages such as MATLAB. Yet, because of its numerous operators, J allows a very succinct code to be used, leading to a tremendous decrease in development time.
SUCCESSIVE LEARNING IN HETERO-ASSOCIATIVE MEMORY USING CHAOTIC NEURAL NETWORKS
Authors: OSANA YUKO , HAGIWARA MASAFUMI
Published: May 2003

In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, it is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman are observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.
HUMAN LEARNING CHARACTERISTICS IN THE TRACKING TASKS OF ITERATIVE NATURE
Authors: ARIF M. , INOOKA H.
Published: May 2003

In this paper, human learning characteristics in the tracking tasks of iterative nature are investigated. Various linear and nonlinear systems are used as plant, and a human operator has to generate the proper control inputs to force these systems in tracking the desired trajectory. The learning behaviour of the human operator in modifying his control actions is studied and it is observed that the human operator can improve his performance quite efficiently despite the unavailability of any information about the system or the desired trajectories. It is concluded from the experiments that the human operator not only use the information that is directly available to him (error in this case), but also extracts some useful information (e.g. error rate) that he feels is necessary to generate a good control action. The limitation of the human performance is studied in frequency domain, and the performance of the human operator against the frequency bandwidth of error and error rate signals are highlighted. Analysis of the results revealed that a human operator gives more importance to the error rate in generating his control actions and, accordingly, it is observed that his limitation in term of performance is more sensitive to the frequency bandwidth of the error rate as compared to the error. The human operator cannot improve his performance once the frequency components of the error or error rates shift to the higher frequencies, say above 1.0 Hz.
A SPACE-TIME DELAY NEURAL NETWORK FOR MOTION RECOGNITION AND ITS APPLICATION TO LIPREADING
Authors: LIN CHIN-TENG , NEIN HSI-WEN , LIN WEN-CHIEH
Published: May 2003

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.
GROWING OF A FUZZY RECURRENT ARTIFICIAL NEURAL NETWORK (FRANN) FOR PATTERN CLASSIFICATION
Authors: BROUWER ROELOF K
Published: May 2003

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.
A NEW NEURAL NETWORK ARCHITECTURE WITH ASSOCIATIVE MEMORY, PRUNING AND ORDER-SENSITIVE LEARNING
Authors: RAO M. SREENIVASA , PUJARI ARUN K.
Published: May 2003

A new paradigm of neural network architecture is proposed that works as associative memory along with capabilities of pruning and order-sensitive learning. The network has a composite structure wherein each node of the network is a Hopfield network by itself. The Hopfield network employs an order-sensitive learning technique and converges to user-specified stable states without having any spurious states. This is based on geometrical structure of the network and of the energy function. The network is so designed that it allows pruning in binary order as it progressively carries out associative memory retrieval. The capacity of the network is 2n, where n is the number of basic nodes in the network. The capabilities of the network are demonstrated by experimenting on three different application areas, namely a Library Database, a Protein Structure Database and Natural Language Understanding.
A FRAMEWORK FOR MODELS OF BIOLOGICAL BEHAVIOUR
Authors: WEBB BARBARA
Published: May 2003

Modelling is most clearly understood as a adjunct in the process of deriving predictions from hypotheses. By representing a hypothesised mechanism in a model we hope by manipulating the model to understand the hypotheses' consequences. Eight dimensions on which models of biological behaviour can vary are described: the degree of realism with which they apply to biology; the level of biology they represent; the generality or range of systems the model is supposed to cover; the abstraction or amount of biological detail represented; the accuracy of representation of the mechanisms; the medium in which the model is built; the match of the model behaviour to biological behaviour; and the utility of the model in providing biological understanding and/or technical insight. It is hoped this framework will help to clarify debates over different approaches to modelling, particularly by pointing out how the above dimensions are relatively independent and should not be conflated.
VISUAL HOMING IN ANALOG HARDWARE
Authors: MÖLLER RALF
Published: May 2003

Insects of several species rely on visual landmarks for returning to important locations in their environment, The "average landmark vector model" is a parsimonious model which reproduces some aspects of the visual homing behavior of bees and ants, To gain insights in the structure and complexity of the neural apparatus that might underly the navigational capabilities of these animals, the average landmark vector model was implemented in analog hardware and used to control a mobile robot. The experiments demonstrate that the apparently complex task of visual homing might be realized by simple and mostly peripheral neural circuits in insect brains.
AN ANALOG VLSI INPLEMENTATION OF A VISUAL INTERNEURON: ENHANCED SENSORY PROCESSING THROUGH BIOPHYSICAL MODELING
Authors: HARRISON REID R. , KOCH CHRISTOF
Published: May 2003

Flies are capable of rapid, coordinated flight through unstructured environments. This flight is guided by visual motion information that is extracted from photoreceptors in a robust manner. One feature of the fly's visual processing that adds to this robustness is the saturation of wide-field motion-sensitive neuron responses with increasing pattern size. This makes the cell's responses less dependent on the sparseness of the optical flow field while retaining motion information. By implementing a compartmental neuronal model in silicon, we add this "gain control" to an existing analog VLSI model of fly vision. This results in enhanced performance in a compact, low-power CMOS motion sensor. Our silicon system also demonstrates that modern, biophysically-detailed models of neural sensory processing systems can be instantiated in VLSI hardware.
A NEUROMORPHIC HAIR SENSOR MODEL OF WIND-MEDIATED ESCAPE IN THE CRICKET
Authors: CHAPMAN TIM , WEBB BARBARA
Published: May 2003

Crickets are able to extract directional information about a wind stimulus through the filiform hairs located on their cerci. This paper describes the design and testing of a neuromorphic sensor that aims to achieve a close correlation with both the physical and functional properties of these hairs. An integrate and fire neural network is used to process the sensory information in real time. The resulting system is shown to be capable of extracting directional information from a wind stimulus and producing an appropriate motor control pattern.
USING A MOBILE ROBOT TO STUDY LOCUST COLLISION AVOIDANCE RESPONSES
Authors: BLANCHARD MARK , VERSCHURE PAUL F. M. J. , RIND F. CLAIRE
Published: May 2003

The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.
INTEGRATING NEUROMORPHIC ACTION-ORIENTED PERCEPTUAL INPUTS TO GENERATE A NAVIGATION BEHAVIOUR FOR A ROBOT
Authors: MUDRA R. , HAHNLOSER R. , DOUGLAS R. J.
Published: May 2003

We use neural networks with pointer map architectures to provide simple attentional processing in a robotic task. A pointer map comprises a map of neurons that encode a stimulus. Besides global feedback inhibition, the map receives feedback excitation via a smalls group of pointer neurons that encode the location of a salient stimulus on the map as a vectorial representation. The pointer neurons are able to apply selective processing to a particular region of the network. The robot uses these properties to manoeuver in relation to an attended object. We implemented a controller composed of two pointer maps, and a motor map. The first pointer map reports the direction of a salient obstacle in a one-dimensional map of distance derived from infrared sensors. The second pointer map reports the direction to potential obstacles in a two-dimensional edge-enhanced image derived from a forward looking CCD-camera. These outputs are applied to a motor map, where they bias the motor control signals issued to the robots wheels, according to navigational intentions.
AN ASIC-CHIP FOR STEREOSCOPIC DEPTH ANALYSIS IN VIDEO-REAL-TIME BASED ON VISUAL CORTICAL CELL BEHAVIOR
Authors: WÖRGÖTTER F.
Published: May 2003

In a stereoscopic system both eyes or cameras have a slightly different view. As a consequence small variations between the projected images exist ("disparities") which are spatially evaluated in order to retrieve depth information [7, 25]. We will show that two related algorithmic versions can be designed which recover disparity. Both approaches are based on the comparison of filter outputs from filtering the left and the right image. The difference of the phase components between left and right filter responses encodes the disparity. One approach uses regular Gabor filters and computes the spatial phase differences in a conventional way as described already in 1988 by Sanger [25]. Novel to this approach, however, is that we formulate it in a way which is fully compatible with neural operations in the visual cortex. The second approach uses the apparently paradoxical similarity between the analysis of visual disparities and the determination of the azimuth of a sound source [27]. Animals determine the direction of the sound from the temporal delay between the left and right ear signals [12]. Similarly, in our second approach [22] we transpose the spatially defined problem of disparity analysis into the temporal domain and utilize two resonators implemented in the form of causal (electronic) filters to determine the disparity as local temporal phase differences between the left and right filter responses. This approach permits video real-time analysis of stereo image sequences (see movies at /Real-Time-Stereo) and a FPGA-based PC-board has been developed which performs stereo-analysis at full PAL resolution in video real-time. An ASIC chip will be available in March 2000.
REAL TIME IMAGE PROCESSING WITH AN ANALOG VISION CHIP SYSTEM
Authors: KAMEDA SEIJI , HONDA AKIRA , YAGI TETSUYA
Published: May 2003

A linear analog network model is proposed to characterize the function of the outer retinal circuit in terms of the standard regularization theory. Inspired by the function and the architecture of the model, a vision chip has been designed using analog CMOS Very Large Scale Integrated circuit technology. In the chip, sample/hold amplifier circuits are incorporated to compensate for statistic transistor mismatches. Accordingly, extremely low noise outputs were obtained from the chip. Using the chip and a zero-crossing detector, edges of given images were effectively extracted in indoor illumination.
A MULTI-DIFFERENTIAL NEUROMORPHIC APPROACH TO MOTION DETECTION
Authors: McOWAN PETER W. , BENTON CHRISTOPHER , DALE JASON , JOHNSTON ALAN
Published: May 2003

This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robusl measure of speed for a range of types of image motion using a single compulational mechanism, forms a usful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.
ACOUSTIC TRANSIENT CLASSIFICATION WITH A TEMPLATE CORRELATION PROCESSOR
Authors: EDWARDS R. TIMOTHY
Published: May 2003

I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.
HUMAN LOCALISATION OF BAND-PASS FILTERED NOISE
Authors: SCHAIK ANDRÉ VAN , JIN CRAIG , CARLILE SIMON
Published: May 2003

In this work we study the influence and relationship of five different acoustical cues to the human sound localisation process. These cues are: interaural time delay, interaural level difference, interaural spectrum, monaural spectrum, and band-edge spectral contrast. Of particular interest was the synthesis and integration of the different cues to produce a coherent and robust percept of spatial location. The relative weighting and role of the different cues was investigated using band-pass filtered white noise with a frequency range (in kHz) of: 0.3–5, 0.3–7, 0.3–10, 0.3–14, 3–8, 4–9, and 7-14. These stimuli provided varying amounts of spectral information and physiologically detectable temporal information, thus probing the localisation process under varying sound conditions. Three subjects with normal hearing in both ears have performed five trials of 76 test positions for each of these stimuli in an anechoic room. All subjects showed systematic mislocalisation on most of these stimuli. The location to which they are mislocalised varies among subjects but in a systematic manner related to the five different acoustical cues. These cues have been correlated with the subject's localisation responses on an individual basis with the results suggesting that the internal weighting of the spectral cues may vary with the sound condition.
A SPIKE-BASED MODEL OF BINAURAL SOUND LOCALIZATION
Authors: SCHAUER CARSTEN , PASCHKE PETER
Published: May 2003

This paper describes a spike-based model of binaural sound localization using interaural time differences (ITDs). To handle the problem of temporal coding and to facilitate a hardware implementation all neurons are simulated by a spike response model, which includes postsynaptic potentials (PSPs) and a refractory period. A winner-take-all (WTA) network selects the dominant source from the representation of the sound's angles of incidences, and can be biased by a multisensory support, We use simulations on real audio data to investigate the function and the practical application of the system.
A BIOLOGICALLY PLAUSIBLE ACOUSTIC MOTION DETECTION NEURAL NETWORK
Authors: CAVACO SOFIA , HALLAM JOHN
Published: May 2003

In this paper we present an acoustic motion detection system to be used in a small mobile robot. While the first purpose of the system has been to be a reliable computational implementation, cheap enough to be built in hardware, effort has also been taken to construct a biologically plausible solution. The motion detector consists of a neural network composed of motion-direction sensitive neurons with a preferred direction and a preferred region of the azimuth. The system was designed to produce a higher response when stimulated by motion in the preferred direction than in the null direction and that is in fact what the system does, which means that, as desired, the system can detect motion and distinguish its direction.
SPEECH-INTELLIGIBILITY IMPROVEMENTS USING A BINAURAL ADAPTIVE-SCHEME BASED CONCEPTUALLY ON HUMAN AUDITORY PROCESSING
Authors: HUSSAIN AMIR , CAMPBELL DOUGLAS R.
Published: May 2003

A speech enhancement scheme is presented using diverse processing in sub-bands spaced according to a human-cochlear describing function. The binaural adaptive scheme decomposes the wide-band input signals into a number of band-limited signals, superficially similar to the treatment the human ears perform on incoming signals. The results of a series of intelligibility and formal listening tests are presented in which acoustic speech signals corrupted with recorded automobile noise were presented to 15 normal hearing volunteer subjects. For the experimental cases considered, the proposed binaural adaptive sub-band processing scheme delivers a statistically significant improvement in terms of both speech-intelligibility and perceived quality when compared with both the conventional wide-band processed and the noisy unprocessed case. The scheme is capable of extension to a potentially more flexible sub-band processing method based on a constrained artificial neural network (ANN).
MULTI-SENSOR NEURAL-NETWORK PROCESSING OF NOISY SPEECH
Authors: HUSSAIN AMIR
Published: May 2003

In this paper, a novel Artificial Neural-Network (ANN) based multi-sensor multi-band adaptive signal-processing scheme is described for enhancing acoustic-speech corrupted by real noise and reverberation. Numerically robust adaptation-algorithms are employed for the ANN based sub-band filters; and, new simulation experiments are reported using real-reverberant automobile data which demonstrate that the proposed speech-enhancement system is capable of outperforming conventional linear filtering-based wide-band and multi-band noise-cancellation schemes.
FAST DIGITAL SIMULATION OF SPIKING NEURAL NETWORKS AND NEUROMORPHIC INTEGRATION WITH SPIKELAB
Authors: GRASSMANN CYPRIAN , ANLAUF JOACHIM K.
Published: May 2003

We present a simulation environment called SPIKELAB which incorporates a simulator that is able to simulate large networks of spiking neurons using a distributed event driven simulation. Contrary to a time driven simulation, which is usually used to simulate spiking neural networks, our simulation needs less computational resources because of the low average activity of typical networks. The paper addresses the speed up using an event driven versus a time driven simulation and how large networks can be simulated by a distribution of the simulation using already available computing resources. It also presents a solution for the integration of digital or analogue neuromorphic circuits into the simulation process.
SPIKEII: AN INTEGRATE-AND-FIRE AVLSI CHIP
Authors: SMITH L. S. , ERIKSSON B. E. , HAMILTON A. , GLOVER M.
Published: May 2003

We present the SPIKEII chip, an integrate-and-fire neural network chip with programmable synapses implemented in analogue VLSI. It is the successor to the SPIKEI chip. We describe the circuitry, and show some results using networks of integrate-and-fire neurons.
STIMULUS CORRELATION AND ADAPTIVE MOTION DETECTION USING SPIKING NEURONS
Authors: ROS E. , PELAYO F. J. , PALOMAR D. , ROJAS I. , BERNIER J. L. , PRIETO A.
Published: May 2003

Stimulus correlation and adaptive movement detection, among other tasks can be performed with VLSI general-purpose neurons that have controllable steady and transient responses. This paper presents experimental results of simple neural primitives based on the CMOS neuron approach described in [11]. Stimulus correlation experiments illustrate the well defined behavior of the CMOS approach. This basic primitive is used to implement motion detectors with adaptive capabilities that enable it to work efficiently in a wide velocity range.
SILICON CELLULAR MORPHOLOGY
Authors: BRESLIN CATHERINE , SMITH LESLIE S.
Published: May 2003

We describe a design technique for neuromorphic engineering that exploits both Mead's notion of physical equivalence between transistors and ion channels and the intrinsic properties of the materials of which transistors are composed. This is in contrast to the "algorithmic" design technique in common use. A "physical" design technique allows us to emphasise the morphology of cells. We hope to use this in an exploration of the effect of cell morphology upon function.
NEOCOGNITRON'S PARAMETER TUNING BY GENETIC ALGORITHMS
Authors: SHI DAMING , DONG CHUNLEI , YEUNG DANIEL S.
Published: May 2003

The further study on the sensitivity analysis of Neocognitron is discussed in this paper. Fukushima's Neocognitron is capable of recognizing distorted patterns as well as tolerating positional shift. Supervised learning of the Neocognitron is fulfilled by training patterns layer by layer. However, many parameters, such as selectivity and receptive fields are set manually. Furthermore, in Fukushima's original Neocognitron, all the training patterns are designed empirically. In this paper, we use Genetic Algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. Four contributions are claimed: first, by analyzing the learning mechanism of Fukushima's original Neocognitron, the correlations amongst the training patterns are claimed to affect the performance of Neocognitron, tuning the Neocognitron's number of planes is equivalent to searching reasonable training patterns for its supervised learning; second, a GA-based supervised learning of the Neocognitron is carried out in this way, searching the parameters and training patterns by GAs but specifying the connection weights by training the Neocognitron; third, other than traditional GAs which are unsuitable for the large searching space of training patterns set, the cooperative coevolution is incorporated to play this role; fourth, an effective fitness function is given out when applying the above methodology into numeral recognition. The evolutionary computation in our initial experiments is implemented based on the original training pattern set, e.g. the individuals of the population are generated from Fukushima's original training patterns during initialization of GAs. The results prove that our correlation analysis is reasonable, and show that the performance of a Neocognitron is sensitive to its training patterns, selectivity and receptive fields, especially, the performance is not monotonically increasing with respect to the number of training patterns, and this GA-based supervised learning is able to improve Neocognitron's performance.
A NEW MEASUREMENT OF NOISE IMMUNITY AND GENERALIZATION ABILITY FOR MLPs
Authors: BERNIER J. L. , ORTEGA J. , ROS E. , ROJAS I. , PRIETO A.
Published: May 2003

This paper shows a quantitative relation between the regularization techniques, the generalization ability, and the sensitivity of the Multilayer Perceptron (MLP) to input noise. Although many studies about these topics have been presented, in most cases only one of the problems is addressed, and only experimentally obtained evidence is provided to illustrate some kind of correlation between generalization, noise immunity and the use of regularization techniques to obtain a set of weights after training that provides the corresponding MLP with generalization ability and noise immunity. Here, a new measurement of noise immunity for a MLP is presented. This measurement, which is termed Mean Squared Sensitivity (MSS), explicitly evaluates the Mean Squared Error (MSE) degradation of a MLP when it is perturbed by input noise, and can be computed from the statistical sensitivities (previously proposed) of the output neurons. The MSS provides an accurate evaluation of the MLP performance loss when its inputs are perturbed by noise and can also be considered a measurement of the smoothness of the error surface with respect to the inputs. Thus, as the MSS can be used to evaluate the noise immunity or the generalization ability, it gives a criterion to select among different weight configurations that present a similar MSE after training.
DNA FRAGMENT ASSEMBLY USING NEURAL PREDICTION TECHNIQUES
Authors: ANGELERI E. , APOLLONI B. , FALCO D. DE , GRANDI L.
Published: May 2003

The paper describes an alternative approach to the fragment assembly problem. The key idea is to train a recurrent neural network to tracking the sequence of bases constituting a given fragment and to assign to a same cluster all the sequences which are well tracked by this network. We make use of a 3-layer Recurrent Perceptron and examine both edited sequences from a ftp site and artificial fragments from a common simulation software: the clusters we obtain exhibit interesting properties in terms of error filtering, stability and self consistency; we define as well, with a certain degree of approximation, a metric on the fragment set. The proposed assembly algorithm is susceptible to becoming an alternative method with the following properties: (i) high quality of the rebuilt genomic sequences, (ii) high parallelizability of the computing process with consequent drastic reduction of the running time.
RECOGNITION OF HANDWRITTEN SIMILAR CHINESE CHARACTERS BY SELF-GROWING PROBABILISTIC DECISION-BASED NEURAL NETWORK
Authors: FU HSIN-CHIA , XU Y. Y. , CHANG H. Y.
Published: May 2003

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters × 200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance.5,11 As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.
A MODULAR NEURAL NETWORK ARCHITECTURE FOR PATTERN CLASSIFICATION BASED ON DIFFERENT FEATURE SETS
Authors: CHEN KE , CHI HUISHENG
Published: May 2003

We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.
MEMORY AND BISTABILITY IN A ONE-DIMENSIONAL LOOP OF MODEL CARDIAC CELLS
Authors: VINET ALAIN
Published: May 2003

Unidirectional propagation has been studied in a one-dimensional loop of model cardiac cells represented as a homogeneous and isotropic cable. Membrane ionic currents were represented by a modified Beeler-Reuter model. The time constants of the gate variables of the slow inward current acting during the plateau of the action potential were divided by a parameter K ≥1. In the space-clamped model, increasing K shortens the action potential duration, changes the shape of the restitution curve and adds a slow memory component to the dynamics. In a paced regime, it promotes bistability in which period-1 and period-2 patterns coexist over an interval of pacing frequencies. In the loop, bistability is created between periodic and aperiodic modes of sustained reentry for an interval of loop length. The bistability of the space-clamped and loop model are both related to the form of the restitution curve.
ROLE OF GAP JUNCTION RESISTANCE IN RATE-INDUCED DELAY IN CONDUCTION IN A CABLE MODEL OF THE ATRIOVENTRICULAR NODE
Authors: JAMALEDDINE RABIH S. , VINET ALAIN
Published: May 2003

A constant fast heart rate results in a time and rate dependent increase in AV nodal conduction time. The origin of this delay in conduction remains unknown due to limited access to corresponding cellular events. We have investigated the possible contribution of gap junction resistance in rate-induced delay in conduction using a one-dimensional model of the AV node.

We use a cable that includes 100 cells: 30 AN cells, 40 N cells and 30 NH cells. The three cell types are modeled by varying the parameters of a modified Beeler Reuter ionic model. N cells have a low density of sodium current and are mainly activated by an ICa,L type current. Their resting potential is less negative and their upstroke slower than those of AN and NH cells. Cells are connected by gap junctions whose resistance is controlled by the intracellular calcium concentration. Stimuli are applied to the first AN cells.

At a pacing rate of 400 ms, the gap junction resistance was AN 1.8 MΩ, N 32.0 MΩ and NH 3.6 MΩ and the total conduction time was 48 ms. Following a sudden decrease of cycle length from 400 ms to 250 ms, the conduction time progressively increased and reached a new steady-state of 77.7 ms after 800 beats. This increase of conduction time was mainly caused by a gradual increase of the gap junction resistance between N cells, which reached 46 MΩ at steady state. Rate and time dependent increase of conduction time was abolished when the gap junction resistances were kept constant.

This model study suggests that the dynamic changes in gap junction resistance may play a key role in rate-induced AV nodal conduction delay.
MEMBRANE AND TISSUE LEVEL CONTRIBUTIONS TO PURKINJE-VENTRICULAR INTERACTIONS: A MODEL STUDY
Authors: HUELSING DELILAH J. , POLLARD ANDREW E.
Published: May 2003

Purkinje-to-ventricular (P-to-V) propagation and electrotonic modulation of repolarization at discrete Purkinje-ventricular junctions (PVJs) depend on differences in the ionic currents and tissue structure of the P network and the V myocardium. We used computer simulations to assess these membrane and tissue level contributions to P-V interactions. At the membrane level, we used the DiFrancesco-Noble membrane equations to model P ionic kinetics and the Luo-Rudy dynamic membrane equations to model V ionic kinetics. At the tissue level, we modeled the P network as a layer of branching cables, and we modeled a single myocardial sheet with an anisotropic layer of excitable cells. P-to-V propagation was enhanced at the tissue level when multiple wavefronts in the branching P network collided at the PVJ. At the membrane level, P-to-V propagation was enhanced by a reduced transient outward current (Ito) in the P layer. Repolarization at the PVJ was also modulated by both membrane and tissue level contributions. Under nominal conditions, action potential duration (APD) shortened in the P layer and prolonged in the V layer. However, when the V mass was reduced, both P and V cell APDs shortened during coupling with nominal Ito. Subsequent Ito inhibition restored coupling-induced prolongation of the V action potential in the reduced V mass. These results suggest that under physiologic conditions, both membrane and tissue level contributions to P-V interactions are important, while membrane level contributions become even more important under pathologies that reduce the difference in P and V tissue size, particularly in the setting of healed myocardial infarction.
DYNAMICS OF VENTRICULAR FIBRILLATION: A MODEL AND EXPERIMENTAL STUDY
Authors: VIGMOND E. , WITKOWSKI F. X. , LEON L. J.
Published: May 2003

Wiggers initially described that after electrical induction of ventricular fibrillation (VF) the rhythm so induced progresses in complexity [14]. Previously Witkowski et al. [15] have described two distinct phases of perfused VF. The acute form was characterized by relatively well organized activity and was observed in the first few minutes after induction. The chronic form developed after this initial reentry and was more disorganized in nature. In this paper we describe computer simulations carried out using a complex three-dimensional model of cardiac tissue. Depending on the time constant of the dynamics of the calcium current, activity ranged from organized scroll waves, to highly disorganized chaotic activity in which it was possible to identify up to 10 different vortex filaments which continually drifted and split. The model data was compared to experimental measurements via the maximum cross-correlation function described previously by Witkowski et al. [15]. We found that the max cross-correlation function for a single scroll wave ranged between 0.6 and 0.9, and is similar to what was experimentally observed from optical transmembrane potential mapping during early VF. For the highly disorganized activity the maximum cross-correlation function was in the range of 0.2, which similarly was in same range reported experimentally for fully developed VF.
ROLE OF CELLULAR COUPLING AND DISPERSION OF REFRACTORINESS IN CARDIAC ARRHYTHMIAS: A SIMULATION STUDY
Authors: SEIGNEURIC R. G. , CHASSÉ J-L. , AUGER P. M. , BARDOU A. L.
Published: May 2003

Computer simulation is applied to study the role of cellular coupling, dispersion of refractoriness as well as both of them, in the mechanisms underlying cardiac arrhythmias. We first assumed that local ischemia mainly induces cell to cell dispersion in the coupling resistance (case 1), refractory period (case 2) or both (case 3). Numerical experiments, based on the van Capelle and Durrer model, showed that vortices could not be induced in these conditions. In order to be more realistic about coronary circulation we simulated a patchy dispersion of cellular properties, each patch corresponding to the zone irrigated by a small coronary artery. In these conditions, a single activation wave could give rise to abnormal activities. Probabilities of reentry, estimated for the three cases cited above, showed that a severe alteration of the coupling resistance may be an important factor in the genesis of reentry. Moreover, use of isochronal maps revealed that vortices were both stable and sustained with an alteration of coupling alone or along with reductions of action potential duration. Conversely, simulations with reduction of the refractoriness alone induced only transient patterns.