I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

An information processing system having signal processors that are interconnected by processing junctions that simulate and extend biological neural networks. As shown in the figure, each processing junction receives signals from one signal processor and generates a new signal to another signal processor. The response of each processing junction is determined by internal junction processes and is continuously changed with temporal variation in the received signal. Different processing junctions connected to receive a common signal from a signal processor respond differently to produce different signals to downstream signal processors. This transforms a temporal pattern of a signal train of spikes into a spatio-temporal pattern of junction events and provides an exponential computational power to signal processors. Each signal processing junction can receive a feedback signal from a downstream signal processor so that an internal junction process can be adjusted to learn certain characteristics embedded in received signals.

Dynamic synapse for signal processing in neural networks

In this paper, a CNN-based spatio-temporal approach is introduced for finding the endocardial (inner) boundary of the left ventricle from a sequence of echocardiographic images. The discussed analogic' CNN algorithm combines optimal non-linear filtering and constrained wave propagation in order to estimate the continuous contour of a moving object in a medium where the edges are ill-defined. In the preprocessing phase, non-linear filtering is employed to remove the coherent speckle noise that corrupts the images. It is verified that an optimal filtering strategy should estimate the mode of the local intensity histogram. Three different approximations of the mode filter were implemented, derived from robust statistics and geometry-driven diffusion, that give an output consistent with the maximum likelihood estimate of the noisy sequence. The kernel of the left ventricle is located and the boundary is found using a fuzzy-adaptive technique that embodies constrained wave propagation. Boundary dislocation, area and smoothness constraints are transformed into the transient length of the CNN while the a priori knowledge about the heart morphology is built into the spatial template parameters (weight values). Special emphasis is given to VLSI implementation complexity. It is shown that the core of the algorithm can be realized using the already available CNN chips. Furthermore, it is argued that all templates of the complete solution belong to the implementation frame that is considered for the next generation of CNN Universal Chips. This study demonstrates that the discussed novel approach allows a reliable endocardial boundary tracking of the left ventricle in real-time using a spatio-temporal CNN visual microprocessor.

CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography

In this paper, we develop a common cellular neural network framework for various adaptive non-linear filters based on robust statistic and geometry-driven diffusion paradigms. The base models of both approaches are defined as difference-controlled non-linear CNN templates, while the self-adjusting property is ensured by simple analogic (analog and logic) CNN algorithms. Two adaptive strategies are shown for the order statistic class. When applied to the images distorted by impulse noise both give more visually pleasing results with lower-frequency weighted mean square error than the median base model. Generalizing a variational approach we derive the constrained anisotropic diffusion, where the output of the geometry-driven diffusion model is forced to stay close to a pre-defined morphological constraint. We propose a coarse-grid CNN approach that is capable of calculating an acceptable noise-level estimate (proportional to the variance of the Gaussian noise) and controlling the fine-grid anisotropic diffusion models. A combined geometrical-statistical approach has also been developed for filtering both the impulse and additive Gaussian noise while preserving the image structure. We briefly discuss how these methods can be embedded into a more complex algorithm performing edge detection and image segmentation. The design strategies are analysed primarily from VLSI implementation point of view; therefore all non-linear cell interactions of the CNN architecture are reduced to two fundamental non-linearities, to a sigmoid type and a radial basis function. The proposed non-linear characteristics can be approximated with simple piecewise-linear functions of the voltage difference of neighbouring cells. The simplification makes it possible to convert all space-invariant non-linear templates of this study to a standard instruction set of the CNN Universal Machine, where each instruction is coded by at most a dozen analog numbers. Examples and simulation results are given throughout the text using various intensity images.

CNN‐based difference‐controlled adaptive non‐linear image filters

In order to investigate whether thalamic synchronization may contribute to the binding of different cortical representations in visual information processing, we made a neuromorphic model of the thalamo-cortical loop, using the cellular neural network (CNN) model and simulator. It is demonstrated with the aid of this model that the cortico-thalamic feedback by synchronizing dLGN relay cell responses, provides a temporal code and this may induce correlated activities in the target cortical neuron population. Our results support the notion that using synchronization of temporally structured activities as general integration mechanism, the processing of visual information occurs simultaneously in the highly interconnected thalamo-cortical system.

CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.(Research report of the Neuromorphic Information Technology Graduate Center. NIT-2-1966.)

In this paper we show cellular neural network (CNN) models of some basic types of cells characterised by diverse spiking patterns. After showing some preliminary models (ion channels, neurons), CNN models of the action potential generation are given followed by an analysis of the rate coding capabilities of the models. Furthermore, CNN models of neurons with diverse intrinsic firing patterns are presented followed by some conclusions. >

http://ieeexplore.ieee.org/iel2/3049/8645/00381709.pdf

Some cortical spiking neuron models using CNN

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Some cortical spiking neuron models using CNN