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

Object recognition apparatus and methods useful for extracting information from sensory input. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded in a pulsed output signal. The information is encoded in one variant as a pattern of pulse latencies relative to an occurrence of a temporal event; e.g., the appearance of a new visual frame or movement of the image. The pattern of pulses advantageously is substantially insensitive to such image parameters as size, position, and orientation, so the image identity can be readily decoded. The size, position, and rotation affect the timing of occurrence of the pattern relative to the event; hence, changing the image size or position will not change the pattern of relative pulse latencies but will shift it in time, e.g., will advance or delay its occurrence.

Apparatus and methods for pulse-code invariant object recognition

Apparatus and methods for plasticity in spiking neuron network. The network may comprise feature-specific units capable of responding to different objects (red and green color). Plasticity mechanism may be configured based on difference between two similarity measures related to activity of different unit types obtained during network training. One similarity measure may be based on activity of units of the same type (red). Another similarity measure may be based on activity of units of one type (red) and another type (green). Similarity measures may comprise a cross-correlogram and/or mutual information determined over an activity window. Several similarity estimates, corresponding to different unit-to-unit pairs may be combined. The combination may comprise a weighted average. During network operation, the activity based plasticity mechanism may be used to potentiate connections between units of the same type (red-red). The plasticity mechanism may be used to depress connections between units of different types (red-green).

Apparatus and methods for activity-based plasticity in a spiking neuron network

Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.

Sensory input processing apparatus in a spiking neural network

Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one embodiment, the plasticity mechanism may be configured for example based on activity of one or more neurons providing feed-forward stimulus and activity of one or more neurons providing inhibitory feedback. When an inhibitory neuron generates an output, inhibitory connections may be potentiated. When an inhibitory neuron receives inhibitory input, the inhibitory connection may be depressed. When the inhibitory input arrives subsequent to the neuron response, the inhibitory connection may be depressed. When input features are unevenly distributed in occurrence, the plasticity mechanism is capable of reducing response rate of neurons that develop receptive fields to more prevalent features. Such functionality may provide network output such that rarely occurring features are not drowned out by more widespread stimulus.

Rate stabilization through plasticity in spiking neuron network

Spike timing dependent plasticity (STDP) learning rule is one of hot topics in neurobiology since it's been widely believed that synaptic plasticity mainly contribute to learning and memory in brain. Up to now, STDP has been observed in a wide variety of areas of brain, hippocampus, cortex and so on. Competition among synapses is an important behavior for this learning rule. In present study, we propose a single layer spiking neural network model using STDP learning rule in inhibitory synapses to investigate the competitive behavior. The experiments show that the synapses among neurons are both strengthened on the whole training process. Thus neurons inhibit the activities of one another, eventually the neuron with the highest input spike rate win the competition. We have found that the behavior is efficient when the differences of firing rates of input neurons without STDP are great than 5Hz, otherwise the winner neuron is random. In order to use the principle to artificial intelligent system, we use a mechanism of dynamic learning rate to let the neuron with the highest input to be selected by the competitive behavior as the winner. Therefore, a robust competitive spiking neural network is obtained.

Competitive behaviors of a spiking neural network with spike timing dependent plasticity

Wavelet transform and texture recognition based on spiking neural network for visual images

This paper presents a parallel processing model based on Hadoop platform for large-scale images processing, which aims to make use of the advantages of high reliability and high scalability of Hadoop distributed platform for distributed memory and distributed computing, so as to achieve the purpose of fast processing of large-scale images. The Hadoop streaming technology is used in the model. The main operations are written on shell script as the mapper of Hadoop streaming, then an assigned filelist is used as the Hadoop streaming's input. The large numbers of image files are delivered to cluster computers for concurrent image processing. The model has been implemented using virtual machines. A set of experimental results and analysis are provided.

A Large-scale Images Processing Model Based on Hadoop Platform

Facial expression recognition is one of challenging topic in image processing. In this paper, a new feature extraction algorithm is proposed for facial expression recognition, in which Gabor filter is combined with 2D complexity for feature extraction. In order to obtain information of texture of expression in a static gray image, the image is transformed to sub images by Gabor wavelet after considerable pretreatment, and then the complexities of sub images are calculated. Fast Principle component analysis (Fast-Pca) is used to reduce the dimensionality of 2D complexity of the sub images and the effectiveness of characteristic vector is tested through an learning vector quantization (LVQ) classifier. The proposed feature extraction algorithm has been successfully applied to the Japanese Female Facial Expression (JAFFE) database with 213 frontal images corresponding 10 different subjects. The images are acquired under variable illumination. Experimental results show that the proposed algorithm obtains low-dimension of features compared with traditional method and expression recognition accuracy is improved.

A feature extraction algorithm based on 2D complexity of gabor wavelets transform for facial expression recognition

The functionalities of spiking neurons can be applied to deal with biological stimuli and explain complicated intelligent behaviorsof the brain. Wavelet transform is a powerful time-frequency analysis tool that can efficiently compress image and extract image features. In this article, a spiking neural network combined with the ON/OFF neuron arrays associated with the human visual system is proposed to perform the fast wavelet transform for visual images. The simulation results show that the spiking neural network can preserve the key features of visual images very well.

Fast Wavelet Transform Based on Spiking Neural Network for Visual Images

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