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Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response...
This paper presents a two stage learning algorithm for a Growing-Pruning Spiking Neural Network (GPSNN) for pattern classification problems. The GPSNN uses three layered network architecture with input layer employing a modified population coding and, leaky integrate-and-fire spiking neurons in the hidden and output layers. The class label for a sample is determined according to the output neuron...
Acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes due to coupling between microphones and loudspeakers. Stereophonic systems provide more realistic experience than single-channel systems, since listeners have spatial information that helps to identify the speaker position. Assuming this scenario, a suitable choice for the system parameters becomes essential...
In some practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over a small, balanced portion of the training data set. Although straightforward, this procedure may discard instances that could be important for the better discrimination of the classes,...
Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions...
We propose a method for music genre classification based on a Self-Organizing Map (SOM) - type network. Music pieces are viewed as sequences of pitch and timbre signals. We define a similarity measure between these sequences, derived from the Levenshtein (edit) distance. In contrast to the standard Levenshtein distance, our similarity measure is able to operate on a continuous vector space. Using...
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large-scale hardware neural...
Artificial Neural Networks are often used as black boxes to implement behavioral functions, developed by trials and errors, fed with sensory inputs and controlled by some criteria of performance. This is the case for pavlovian conditioning where important sensory information is non ambiguous and where the error of prediction is to be minimized. These past years, taking into account critical conditioning...
This paper proposes novel algorithms for data-point and feature selection of motor imagery electroencephalographic signals for classifying motor plannings involved in car- driving including braking, acceleration, left steering control and right steering control. Variants of neural network classifiers such as linear support vector machines, and kernel-based support vector machines including radial...
The brain of the fruit fly Drosophila Melanogaster is an attractive system for studying the logic underlying neural circuits because it implements a rich behavior repertoire with a number of neural components that is five orders of magnitude smaller than that of vertebrates. Analysis of the fly's connectome using a powerful toolkit of well-developed genetic techniques and advanced electrophysiological...
The social coupons industry, such as Groupon and LivingSocial, has experienced explosive growth in recent years. Social coupons that combine the features of daily deals and group buying create a new business strategy. In previous literature, theoretical hypotheses regarding this novel strategy have been developed. However, empirical investigation using real world data is few. In this paper, we aim...
The automatic design of control systems for multi-robot teams that operate in real time is not affordable with traditional evolutionary algorithms mainly due to the huge computational requirements they imply. Embodied Evolution (EE) is an evolutionary paradigm that aims to address this problem through the embodiment of the individuals that make up the population in the physical robots. The interest...
Conditional nonlinear optimal perturbation (CNOP) is an extension of linear singular vector(LSV) to nonlinear optimization. Generally, CNOP is solved with such adjoint based algorithms as SPG2, SQP. Unfortunately, it is often difficult to obtain the corresponding adjoint models for some nonlinear models. In addition, for nonlinear models containing discontinuous “on-off” switches, the adjoint based...
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